Control system for plant

ABSTRACT

A control system for a plant which controls the plant using an identifier and a sliding mode controller. The identifier identifies a model parameter vector of a controlled object model of the plant. The sliding mode controller controls the plant using the model parameter vector identified by the identifier. According to the identifier, an identifying error of the model parameter vector is calculated and an updating vector is calculated according to the identifying error. The updating vector is corrected by multiplying a past value of at least one element of the updating vector by a predetermined value which is greater than “0” and less than “1”. The model parameter vector is calculated by adding the corrected updating vector to a reference vector of the model parameter vector.

BACKGROUND OF THE INVENTION

[0001] The present invention relates to a control system for a plant, and more particularly to a control system for controlling a plant with a sliding mode controller based on a sliding mode control theory which is one of robust control theories.

[0002] One known control system based on a sliding mode control theory is disclosed in Japanese Patent Laid-open No. Hei 9-274504, for example. The publication proposes a method of setting a hyperplane in the sliding mode control theory according to the convergence state of a controlled state quantity. According to the proposed method, the convergence response and convergence stability of the sliding mode control is improved.

[0003] For controlling a plant, which is a controlled object, with the sliding mode controller, it is necessary to produce a model of the plant and determine model parameters representing the characteristics of the model of the plant (i.e. the controlled object). The model parameters may be set to predetermined constant values. However, the values of the model parameters usually change due to aging and disturbance. Therefore, it is desirable to use a model parameter identifier for identifying the model parameters on a real-time basis and carry out the sliding mode control using the model parameters that are identified by the model parameter identifier.

[0004] The model parameter identifier detects an identifying error which is a difference between the output of the plant which is calculated using the identified model parameters and the actual output of the plant, and corrects the model parameters in order to eliminate the identifying error. Therefore, regarding the model parameter identifier, the following problems may occur.

[0005] Due to nonlinear characteristics and disturbance whose average value is not “0”, the identifying error does not become “0” even though substantially optimum model parameters have actually been obtained. Therefore, the model parameters which do not need to be corrected are occasionally corrected. As a result, a drift occurs in which the values of the model parameters gradually shift from their optimum values to some other values to make the control performed by the sliding mode controller unstable.

SUMMARY OF THE INVENTION

[0006] It is therefore an object of the present invention to provide a control system for a plant, which can control the plant more stably when the model parameters of the controlled object model which are obtained by modeling the plant, which is a controlled object, are identified and the sliding mode control is performed using the identified model parameters.

[0007] To achieve the above object, the present invention provides a control system for a plant, comprising identifying means and a sliding mode controller. The identifying means identifies a model parameter vector (θ) of a controlled object model of a plant which is obtained by modeling the plant. The sliding mode controller controls the plant using the model parameter vector identified by the identifying means.

[0008] The identifying means comprises an identifying error calculating means, an updating vector calculating means, and an updating vector correcting means. The identifying error calculating means calculates an identifying error (ide) of the model parameter vector. The updating vector calculating means calculates an updating vector (dθ) according to the identifying error. The updating vector correcting means corrects the updating vector by multiplying a past value of at least one element of the updating vector by a predetermined value (DELTAi, EPSi) which is greater than “0” and less than “1”. The identifying means calculates the model parameter vector by adding the corrected updating vector to a reference vector (θbase, θ(0)) of the model parameter vector.

[0009] With this configuration, the updating vector is calculated according to the identifying error of the model parameter vector, and corrected by multiplying the past value of at least one element of the updating vector by the predetermined value which is greater than “0” and less than “1”. The corrected updating vector is added to the reference vector of the model parameter vector to calculate the model parameter vector. Accordingly, values of the elements of the updating vector are limited, thus stabilizing the model parameter vector in the vicinity of the reference vector. As a result, the drift of the model parameters is prevented, to thereby improve the stability of the sliding mode control performed by the sliding mode controller.

[0010] Preferably, the updating vector correcting means does not multiply one of an element of the updating vector which is relevant to the input of the plant (an element relevant to the calculation of b1) and an element of the updating vector which is irrelevant to the input and the output of the plant (an element related to the calculation of c1), by the predetermined value. The parameter b1 is an element of the model parameter vector which is multiplied to the input of the plant. The parameter c1 is an element of the model parameter vector which is irrelevant to the input and output of the plant.

[0011] With this configuration, none of the element of the updating vector which is relevant to the input of the plant and the element of the updating vector which is irrelevant to the input and the output of the plant is multiplied by the predetermined value which is greater than “0” and less than “1”. The steady-state deviation which may be caused by correcting these elements can be prevented from occurring.

[0012] Preferably, the updating vector correcting multiplies at least one element of the reference vector (θ(0)) by the predetermined value (EPSi). With this configuration, at least one element of the reference vector is multiplied by the predetermined value to calculate the model parameter vector. Also in this case, the drift of the model parameters is prevented, to thereby improve the stability of the sliding mode control performed by the sliding mode controller.

[0013] The present invention further provides a control system for a plant, comprising an identifying means and a sliding mode controller. The identifying means identifies a model parameter vector of a controlled object model which is obtained by modeling the plant, based on an input and an output of the plant. The sliding mode controller controls the plant using the model parameter vector identified by said identifying means. The identifying means comprises identifying error calculating means and identifying error correcting means. The identifying error calculating means calculates an identifying error (ide) of the model parameter vector. The identifying error correcting means corrects the identifying error in a decreasing direction, if the identifying error is in a predetermined range (−EIDNRLM≦ide≦EIDNRLMT). The identifying means calculates the model parameter vector using the identifying error (idenl) corrected by the identifying error correcting means.

[0014] With this configuration, if the identifying error is in the predetermined range, the identifying error is corrected in a decreasing direction, and the model parameter vector is calculated using the corrected identifying error. Therefore, the identifying error is suppressed to less accumulate in the model parameters, that is, the drift of the model parameters is prevented, to thereby improve the stability of the sliding mode control carried out by the sliding mode controller.

[0015] Preferably, the identifying error correcting means sets the identifying error to “0”, if the identifying error is in the predetermined range. With this configuration, if the identifying error is in the predetermined range, the identifying error is set to “0”. Therefore, the effect of the identifying error which is not to be reflected to the values of model parameters is eliminated, resulting in an increased effect of preventing the model parameters from drifting.

[0016] Preferably the predetermined range is set according to an amount (DDTHR) of change in a control target value or an amount (DTH) of change in the output of the plant. With this configuration, the predetermined range is set according to the amount of change in the control target value or the amount of change in the output of the plant. Therefore, it is prevented that the identifying error which is to be reflected to the values of model parameters is reduced or neglected as an unnecessary error.

[0017] Preferably, the identifying means identifies the model parameter vector according to a fixed gain algorithm. With this configuration, the model parameter vector is calculated according to the fixed gain algorithm. Accordingly, the amount of calculations for identifying the model parameters can be reduced.

[0018] Preferably, the identifying error calculating means performs a low-pass filtering of the identifying error and outputs the identifying error after the low-pass filtering. With this configuration, the model parameter vector is identified using the identifying error which has been processed by the low-pass filtering. Accordingly, the frequency characteristics of the controlled object model are made closer to the actual frequency characteristics of the plant, to thereby improve the robustness and stability of the control carried out by the sliding mode controller.

[0019] Preferably, the control system further comprises predicting means for calculating a predicted value (PREDTH) of the output of the plant. With this configuration, the predicted value of the output of the plant is calculated by the predicting means. Therefore, the plant which has a dead time element can be controlled accurately.

[0020] Preferably, the predicting means calculates the predicted value using the model parameter vector identified by the identifying means. With this configuration, the predicted value is calculated using the model parameter vector identified by the identifying means. Therefore, it is possible to calculate an accurate predicted value even when the dynamic characteristics of the plant changes with time or changes according to environmental conditions.

[0021] Preferably, a control input applied from the adaptive sliding mode controller to the plant includes an adaptive law input. With this configuration, the control input applied to the plant includes the adaptive law input. Accordingly, good controllability can be obtained even in the presence of disturbance and/or a modeling error, which is a difference between the characteristics of the actual plant and the characteristics of the controlled object model.

[0022] More specifically, the plant includes a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating the throttle valve, and the sliding mode controller calculates a parameter for determining a control input to be applied to the throttle valve actuating device to make an opening of the throttle valve coincide with a target opening. With this configuration, the sliding mode controller performs the control to make the opening of the throttle valve coincide with the target opening, using stable model parameters identified by the identifying means. Consequently, the controllability of the throttle valve opening to the target opening can be improved, and the throttle valve opening can be controlled more stably.

[0023] The above and other objects, features, and advantages of the present invention will become apparent from the following description when taken in conjunction with the accompanying drawings which illustrate embodiments of the present invention by way of example.

BRIEF DESCRIPTION OF THE DRAWINGS

[0024]FIG. 1 is a schematic view of a throttle valve control system according to a first embodiment of the present invention;

[0025]FIGS. 2A and 2B are diagrams showing frequency characteristics of the throttle valve actuating device shown in FIG. 1;

[0026]FIG. 3 is a functional block diagram showing functions realized by an electronic control unit (ECU) shown in FIG. 1;

[0027]FIG. 4 is a diagram showing the relationship between control characteristics of a sliding mode controller and the value of a switching function setting parameter (VPOLE);

[0028]FIG. 5 is a diagram showing a range for setting control gains (F, G) of the sliding mode controller;

[0029]FIGS. 6A and 6B are diagrams illustrative of a drift of model parameters;

[0030]FIGS. 7A through 7C are diagrams showing functions for correcting an identifying error;

[0031]FIG. 8 is a diagram illustrating that a default opening deviation of a throttle valve is reflected to a model parameter (c1′);

[0032]FIG. 9 is a flowchart showing a throttle valve opening control process;

[0033]FIG. 10 is a flowchart showing a process of setting state variables in the process shown in FIG. 9;

[0034]FIG. 11 is a flowchart showing a process of performing calculations of a model parameter identifier in the process shown in FIG. 9;

[0035]FIG. 12 is a flowchart showing a process of calculating an identifying error (ide) in the process shown in FIG. 11;

[0036]FIGS. 13A and 13B are diagrams illustrative of a process of low-pass filtering on the identifying error (ide);

[0037]FIG. 14 is a flowchart showing the dead zone process in the process shown in FIG. 12;

[0038]FIG. 15 is a diagram showing a table used in the process shown in FIG. 14;

[0039]FIG. 16 is a flowchart showing a process of stabilizing a model parameter vector (θ) in the process shown in FIG. 11;

[0040]FIG. 17 is a flowchart showing a limit process of model parameters (a1′, a2′) in the process shown in FIG. 16;

[0041]FIG. 18 is a diagram illustrative of the change in the values of the model parameters in the process shown in FIG. 16;

[0042]FIG. 19 is a flowchart showing a limit process of a model parameter (b1′) in the process shown in FIG. 16;

[0043]FIG. 20 is a flowchart showing a limit process of a model parameter (c1′) in the process shown in FIG. 16;

[0044]FIG. 21 is a flowchart showing a process of performing calculations of a state predictor in the process shown in FIG. 9;

[0045]FIG. 22 is a flowchart showing a process of calculating a control input (Usl) in the process shown in FIG. 9;

[0046]FIG. 23 is a flowchart showing a process of calculating a predicted switching function value (σpre) in the process shown in FIG. 22;

[0047]FIG. 24 is a flowchart showing a process of calculating the switching function setting parameter (VPOLE) in the process shown in FIG. 23;

[0048]FIGS. 25A through 25C are diagrams showing maps used in the process shown in FIG. 24;

[0049]FIG. 26 is a flowchart showing a process of calculating an integrated value of the predicted switching function value (σpre) in the process shown in FIG. 22;

[0050]FIG. 27 is a flowchart showing a process of calculating a reaching law input (Urch) in the process shown in FIG. 22;

[0051]FIG. 28 is a flowchart showing a process of calculating an adaptive law input (Uadp) in the process shown in FIG. 22;

[0052]FIG. 29 is a flowchart showing a process of determining the stability of the sliding mode controller in the process shown in FIG. 9;

[0053]FIG. 30 is a flowchart showing a process of calculating a default opening deviation (thdefadp) in the process shown in FIG. 9;

[0054]FIG. 31 is a functional block diagram showing functions realized by the electronic control unit (ECU) shown in FIG. 1 according to a second embodiment of the present invention;

[0055]FIG. 32 is a flowchart showing a throttle valve opening control process according to the second embodiment;

[0056]FIG. 33 is a flowchart showing a process of performing calculations of a model parameter identifier in the process shown in FIG. 32;

[0057]FIG. 34 is a diagram showing a table used in the process shown in FIG. 33;

[0058]FIG. 35 is a flowchart showing a process of calculating an identifying error (ide) in the process shown in FIG. 33;

[0059]FIG. 36 is a flowchart showing a process of calculating a control input (Usl) in the process shown in FIG. 32;

[0060]FIG. 37 is a flowchart showing a process of calculating a switching function value (σ) in the process shown in FIG. 36;

[0061]FIG. 38 is a flowchart showing a process of calculating an integrated value of the switching function value (σ) in the process shown in FIG. 36;

[0062]FIG. 39 is a flowchart showing a process of calculating a reaching law input (Urch) in the process shown in FIG. 36;

[0063]FIG. 40 is a flowchart showing a process of calculating an adaptive law input (Uadp) in the process shown in FIG. 36;

[0064]FIG. 41 is a flowchart showing a process of determining the stability of a sliding mode controller in the process shown in FIG. 32;

[0065]FIG. 42 is a block diagram of a control system according to a third embodiment of the present invention;

[0066]FIG. 43 is a block diagram of a modification of the control system shown in FIG. 42;

[0067]FIG. 44 is a block diagram of a control system according to a fourth embodiment of the present invention; and

[0068]FIG. 45 is a block diagram of a modification of the control system shown in FIG. 44.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0069] First Embodiment

[0070]FIG. 1 schematically shows a configuration of a throttle valve control system according to a first embodiment of the present invention. An internal combustion engine (hereinafter referred to as “engine”) 1 has an intake passage 2 with a throttle valve 3 disposed therein. The throttle valve 3 is provided with a return spring 4 as a first energizing means for energizing the throttle valve 3 in a closing direction, and a resilient member 5 as a second energizing means for energizing the throttle valve 3 in an opening direction. The throttle valve 3 can be actuated by a motor 6 as an actuating means through gears (not shown). When the actuating force from the motor 6 is not applied to the throttle valve 3, an opening TH of the throttle valve 3 is maintained at a default opening THDEF (for example, 5 degrees) where the energizing force of the return spring 4 and the energizing force of the resilient member 5 are in equilibrium.

[0071] The motor 6 is connected to an electronic control unit (hereinafter referred to as “ECU”) 7. The operation of the motor 6 is controlled by the ECU 7. The throttle valve 3 is associated with a throttle valve opening sensor 8 for detecting the throttle valve opening TH. A detected signal from the throttle valve opening sensor 8 is supplied to the ECU 7.

[0072] Further, the ECU 7 is connected to an acceleration sensor 9 for detecting a depression amount ACC of an accelerator pedal to detect an output demanded by the driver of the vehicle on which the engine 1 is mounted. A detected signal from the acceleration sensor 9 is supplied to the ECU 7.

[0073] The ECU 7 has an input circuit, an A/D converter, a central processing unit (CPU), a memory circuit, and an output circuit. The input circuit is supplied with detected signals from the throttle valve opening sensor 8 and the acceleration sensor 9. The A/D converter converts input signals into digital signals. The CPU carries out various process operations. The memory circuit has a ROM (read only memory) for storing processes executed by the CPU, and maps and tables that are referred to in the processes, a RAM for storing results of executing processes by the CPU. The output circuit supplies an energizing current to the motor 6. The ECU 7 determines a target opening THR of the throttle valve 3 according to the depression amount ACC of the accelerator pedal, determines a control quantity DUT for the motor 6 in order to make the detected throttle valve opening TH coincide with the target opening THR, and supplies an electric signal according to the control quantity DUT to the motor 6.

[0074] In the present embodiment, a throttle valve actuating device 10 that includes the throttle valve 3, the return spring 4, the resilient member 5, and the motor 6 is a controlled object. An input to be applied to the controlled object is a duty ratio DUT of the electric signal applied to the motor 6. An output from the controlled object is the throttle valve opening TH detected by the throttle valve opening sensor 8.

[0075] When frequency response characteristics of the throttle valve actuating device 10 are measured, gain characteristics and phase characteristics indicated by the solid lines in FIGS. 2A and 2B are obtained. A model defined by the equation (1) shown below is set as a controlled object model. Frequency response characteristics of the model are indicated by the broken-line curves in FIGS. 2A and 2B. It has been confirmed that the frequency response characteristics of the model are similar to the characteristics of the throttle valve actuating device 10.

DTH(k+1)=a1×DTH(k)+a2×DTH(k−1)+b1×DUT(k−d)+c1  (1)

[0076] where k is a parameter representing discrete time, and DTH(k) is a throttle valve opening deviation amount defined by the equation (2) shown below. DTH(k+1) is a throttle valve opening deviation amount at a discrete time (k+1).

DTH(k)=TH(k)−THDEF  (2)

[0077] where TH is a detected throttle valve opening, and THDEF is the default opening.

[0078] In the equation (1), a1, a2, b1, and c1 are parameters determining the characteristics of the controlled object model, and d is a dead time. The dead time is a delay between the input and output of the controlled object model.

[0079] The model defined by the equation (1) is a DARX model (delayed autoregressive model with exogeneous input) of a discrete time system, which is employed for facilitating the application of an adaptive control.

[0080] In the equation (1), the model parameter c1 which is irrelevant to the input and output of the controlled object, in addition to the model parameters a1 and a2 which are relevant to the output deviation amount DTH and the model parameter b1 which is relevant to the input duty ratio DUT. The model parameter c1 is a parameter representing a deviation amount of the default opening THDEF and disturbance applied to the throttle valve actuating device 10. In other words, the default opening deviation amount and the disturbance can be identified by identifying the model parameter c1 simultaneously with the model parameters a1, a2, and b1 by a model parameter identifier.

[0081]FIG. 3 is a functional block diagram of the throttle valve control system which is realized by the ECU 7. The throttle valve control system as configured includes, an adaptive sliding mode controller 21, a model parameter identifier 22, a state predictor 23 for calculating a predicted throttle valve opening deviation amount (hereinafter referred to as “predicted deviation amount” or PREDTH(k)) where PREDTH(k) (=DTH(k+d)) after the dead time d has elapsed, and a target opening setting unit 24 for setting a target opening THR for the throttle valve 3 according to the accelerator pedal depression amount ACC.

[0082] The adaptive sliding mode controller 21 calculates a duty ratio DUT according to an adaptive sliding mode control in order to make the detected throttle valve opening TH coincide with the target opening THR, and outputs the calculated duty ratio DUT.

[0083] By using the adaptive sliding mode controller 21, it is possible to change the response characteristics of the throttle valve opening TH to the target opening THR, using a specific parameter (VPOLE). As a result, it is possible to avoid shocks at the time the throttle valve 3 moves from an open position to a fully closed position, i.e., at the time the throttle valve 3 collides with a stopper for stopping the throttle valve 3 in the fully closed position. It is also possible to make the engine response corresponding to the operation of the accelerator pedal variable. Further, it is also possible to obtain a good stability against errors of the model parameters.

[0084] The model parameter identifier 22 calculates a corrected model parameter vector θL (θL^(T)=[a1, a2, b1, c1]) and supplies the calculated corrected model parameter vector θL to the adaptive sliding mode controller 21. More specifically, the model parameter identifier 22 calculates a model parameter vector θ based on the throttle valve opening TH and the duty ratio DUT. The model parameter identifier 22 then carries out a limit process of the model parameter vector θ to calculate the corrected model parameter vector θL, and supplies the corrected model parameter vector θL to the adaptive sliding mode controller 21. In this manner, the model parameters a1, a2, and b1 which are optimum for making the throttle valve opening TH follow up the target opening THR are obtained, and also the model parameter c1 indicative of disturbance and a deviation amount of the default opening THDEF is obtained.

[0085] By using the model parameter identifier 22 for identifying the model parameters on a real-time basis, adaptation to changes in engine operating conditions, compensation for hardware characteristics variations, compensatation for power supply voltage fluctuations, and adaptation to aging-dependent changes of hardware characteristics are possible.

[0086] The state predictor 23 calculates a throttle valve opening TH (predicted value) after the dead time d has elapsed, or more specifically a predicted deviation amount PREDTH, based on the throttle valve opening TH and the duty ratio DUT, and supplies the calculated deviation amount PREDTH to the adaptive sliding mode controller 21. By using the predicted deviation amount PREDTH, the robustness of the control system against the dead time of the controlled object is ensured, and the controllability in the vicinity of the default opening THDEF where the dead time is large is improved.

[0087] Next, principles of operation of the adaptive sliding mode controller 21 will be hereinafter described.

[0088] First, a target value DTHR(k) is defined as a deviation amount between the target opening THR(k) and the default opening THDEF by the following equation (3).

DTHR(k)=THR(k)−THDEF  (3)

[0089] If a deviation e(k) between the throttle valve opening deviation amount DTH and the target value DTHR is defined by the following equation (4), then a switching function value σ(k) of the adaptive sliding mode controller is set by the following equation (5).

e(k)=DTH(k)−DTHR(k)  (4)

σ(k)=e(k)+VPOLE×e(k−1)=(DTH(k)−DTHR(k))+VPOLE×(DTH(k−1)−DTHR(k−1))  (5)

[0090] where VPOLE is a switching function setting parameter that is set to a value which is greater than −1 and less than 1.

[0091] On a phase plane defined by a vertical axis representing the deviation e(k) and a horizontal axis representing the preceding deviation e(k−1), a pair of the deviation e(k) and the preceding deviation e(k−1) satisfying the equation of “σ(k)=0” represents a straight line. The straight line is generally referred to as a switching straight line. A sliding mode control is a control contemplating the behavior of the deviation e(k) on the switching straight line. The sliding mode control is carried out so that the switching function value σ(k) becomes 0, i.e., the pair of the deviation e(k) and the preceding deviation e(k−1) exists on the switching straight line on the phase plane, to thereby achieve a robust control against disturbance and the modeling error (the difference between the characteristics of an actual plant and the characteristics of a controlled object model). As a result, the throttle valve opening deviation amount DTH is controlled with good robustness to follow up the target value DTHR.

[0092] As shown in FIG. 4, by changing the value of the switching function setting parameter VPOLE in the equation (5), it is possible to change damping characteristics of the deviation e(k), i.e., the follow-up characteristics of the throttle valve opening deviation amount DTH to follow the target value DTHR. Specifically, if VPOLE equals −1, then the throttle valve opening deviation amount DTH completely fails to follow up the target value DTHR. As the absolute value of the switching function setting parameter VPOLE is reduced, the speed at which the throttle valve opening deviation amount DTH follows up the target value DTHR increases.

[0093] The throttle valve control system is required to satisfy the following requirements A1 and A2:

[0094] A1) When the throttle valve 3 is shifted to the fully closed position, collision of the throttle valve 3 with the stopper for stopping the throttle valve 3 in the fully closed position should be avoided; and

[0095] A2) The controllability with respect to the nonlinear characteristics in the vicinity of the default opening THDEF (a change in the resiliency characteristics due to the equilibrium between the energizing force of the return spring 4 and the energizing force of the resilient member 5, backlash of gears interposed between the motor 6 and the throttle valve 3, and a dead zone where the throttle valve opening does not change even when the duty ratio DUT changes) should be improved.

[0096] Therefore, it is necessary to lower the speed at which the deviation e(k) converges, i.e., the converging speed of the deviation e(k), in the vicinity of the fully closed position of the throttle valve, and to increase the converging speed of the deviation e(k) in the vicinity of the default opening THDEF.

[0097] According to the sliding mode control, the converging speed of e(k) can easily be changed by changing the switching function setting parameter VPOLE. Therefore in the present embodiment, the switching function setting parameter VPOLE is set according to the throttle valve opening TH and an amount of change DDTHR (=DTHR(k)−DTHR(k−1)) of the target value DTHR, to thereby satisfy the requirements A1 and A2.

[0098] As described above, according to the sliding mode control, the deviation e(k) is converged to 0 at an indicated converging speed and robustly against disturbance and the modeling error by constraining the pair of the deviation e(k) and the preceding deviation e(k−1) on the switching straight line (the pair of e(k) and e(k−1) will be hereinafter referred to as “deviation state quantity”). Therefore, in the sliding mode control, it is important how to place the deviation state quantity onto the switching straight line and constrain the deviation state quantity on the switching straight line.

[0099] From the above standpoint, an input DUT(k) (also indicated as Usl(k)) to the controlled object (an output of the controller) is expressed as the sum of an equivalent control input Ueq(k), a reaching law input Urch(k), and an adaptive law input Uadp(k), as indicated by the following equation (6).

DUT(k)=Usl(k)=Ueq(k)+Urch(k)+Uadp(k)  (6)

[0100] The equivalent control input Ueq(k) is an input for constraining the deviation state quantity on the switching straight line. The reaching law input Urch(k) is an input for placing deviation state quantity onto the switching straight line. The adaptive law input Uadp(k) is an input for placing deviation state quantity onto the switching straight line while reducing the modeling error and the effect of disturbance. Methods of calculating these inputs Ueq(k), Urch(k), and Uadp(k) will be described below.

[0101] Since the equivalent control input Ueq(k) is an input for constraining the deviation state quantity on the switching straight line, a condition to be satisfied is given by the following equation (7):

σ(k)=σ(k+1)  (7)

[0102] Using the equations (1), (4), and (5), the duty ratio DUT(k) satisfying the equation (7) is determined by the equation (9) shown below. The duty ratio DUT(k) calculated with the equation (9) represents the equivalent control input Ueq(k). The reaching law input Urch(k) and the adaptive law input Uadp(k) are defined by the respective equations (10) and (11) shown below. $\begin{matrix} \begin{matrix} {{{DUT}(k)} = \quad {\frac{1}{b1}\left\{ {{\left( {1 - {a1} - {VPOLE}} \right){{DTH}\left( {k + d} \right)}} +} \right.}} \\ {\quad {{\left( {{VPOLE} - {a2}} \right){{DTH}\left( {k + d - 1} \right)}} -}} \\ {\quad {{c1} + {{DTHR}\left( {k + d + 1} \right)} +}} \\ {\quad {{\left( {{VPOLE} - 1} \right){{DTHR}\left( {k + d} \right)}} -}} \\ {\quad \left. {{VPOLE} \times {{DTHR}\left( {k + d - 1} \right)}} \right\}} \\ {= \quad {{Ueq}(k)}} \end{matrix} & (9) \\ {{{Urch}(k)} = {\frac{- F}{b1}{\sigma \left( {k + d} \right)}}} & (10) \\ {{{Uadp}(k)} = {\frac{- G}{b1}{\sum\limits_{i = 0}^{k + d}{\Delta \quad T\quad {\sigma (i)}}}}} & (11) \end{matrix}$

[0103] where F and G respectively represent a reaching law control gain and an adaptive law control gain, which are set as described below, and ΔT represents a control period.

[0104] Calculating the equation (9) requires a throttle valve opening deviation amount DTH(k+d) after the elapse of the dead time d and a corresponding target value DTHR(k+d+1). Therefore, the predicted deviation amount PREDTH(k) calculated by the state predictor 23 is used as the throttle valve opening deviation amount DTH(k+d) after the elapse of the dead time d, and the latest target value DTHR is used as the target value DTHR(k+d+1).

[0105] Next, the reaching law control gain F and the adaptive law control gain G are determined so that the deviation state quantity can stably be placed onto the switching straight line by the reaching law input Urch and the adaptive law input Uadp.

[0106] Specifically, a disturbance V(k) is assumed, and a stability condition for keeping the switching function value σ(k) stable against the disturbance V(k) are determined to obtain a condition for setting the gains F and G. As a result, it has been obtained as the stability condition that the combination of the gains F and G satisfies the following equations (12) through (14), in other words, the combination of the gains F and G should be located in a hatched region shown in FIG. 5.

F>0  (12)

G>0  (13)

F<2−(ΔT/2)G  (14)

[0107] As described above, the equivalent control input Ueq(k), the reaching law input Urch(k), and the adaptive law input Uadp(k) are calculated from the equations (9) through (11), and the duty ratio DUT(k) is calculated as the sum of those inputs.

[0108] The model parameter identifier 22 calculates a model parameter vector of the controlled object model, based on the input (DUT(k)) and output (TH(k)) of the controlled object, as described above. Specifically, the model parameter identifier 22 calculates a model parameter vector θ(k) according to a sequential identifying algorithm (generalized sequential method-of-least-squares algorithm) represented by the following equation (15).

θ(k)=θ(k−1)+KP(k)ide(k)  (15)

σ(k)^(T) =[a1′, a2′, b1′, c1′]  (16)

[0109] where a1′, a2′, b1′, c1′ represent model parameters before a limit process described later is carried out, ide(k) represents an identifying error defined by the equations (17), (18), and (19) shown below, where DTHHAT(k) represents an estimated value of the throttle valve opening deviation amount DTH(k) (hereinafter referred to as “estimated throttle valve opening deviation amount”) which is calculated using the latest model parameter vector θ(k−1), and KP(k) represents a gain coefficient vector defined by the equation (20) shown below. In the equation (20), P(k) represents a quartic square matrix calculated from the equation (21) shown below.

ide(k)=DTH(k)−DTHHAT(k)  (17)

DTHHAT(k)=θ(k−1)^(T)ζ(k)  (18)

ζ(k)^(T) =[DTH(k−1), DTH(k−2), DUT(k−d−1), 1]  (19) $\begin{matrix} {{{KP}(k)} = \frac{{P(k)}{\zeta (k)}}{1 + {{\zeta^{T}(k)}{P(k)}{\zeta (k)}}}} & (20) \\ \begin{matrix} {{P\left( {k + 1} \right)} = \quad {\frac{1}{\lambda_{1}}\left( {- \frac{\lambda_{2}{P(k)}{\zeta (k)}{\zeta^{T}(k)}}{\lambda_{1} + {\lambda_{2}{\zeta^{T}(k)}{P(k)}{\zeta (k)}}}} \right){P(k)}}} \\ {\quad \left( {:{{Identity}\quad {Matrix}}} \right)} \end{matrix} & (21) \end{matrix}$

[0110] In accordance with the setting of coefficients λ1 and λ2 in the equation (21), the identifying algorithm from the equations (15) through (21) becomes one of the following four identifying algorithm:

[0111] λ1=1, λ2=0

[0112] Fixed gain algorithm

[0113] λ1=1, λ2=1

[0114] Method-of-least-squares algorithm

[0115] λ1=1, λ2=λ

[0116] Degressive gain algorithm

[0117] (λ is a given value other than 0, 1)

[0118] λ1=λ, λ2=1

[0119] Weighted Method-of-least-squares algorithm

[0120] (λ is a given value other than 0, 1)

[0121] In the present embodiment, it is required that the following requirements B1, B2, and B3 are satisfied:

[0122] B1) Adaptation to Quasi-Static Dynamic Characteristics Changes and Hardware Characteristics Variations

[0123] “Quasi-static dynamic characteristics changes” mean slow-rate characteristics changes such as power supply voltage fluctuations or hardware degradations due to aging.

[0124] B2) Adaptation to High-Rate Dynamic Characteristics Changes

[0125] Specifically, this means adaptation to dynamic characteristics changes depending on changes in the throttle valve opening TH.

[0126] B3) Prevention of a Drift of Model Parameters

[0127] The drift, which is an excessive increase of the absolute values of model parameters, should be prevented. The drift of model parameters is caused by the effect of the identifying error, which should not be reflected to the model parameters, due to nonlinear characteristics of the controlled object.

[0128] In order to satisfy the requirements B1 and B2, the coefficients λ1 and λ2 are set respectively to a given value λ and “0” so that the weighted Method-of-least-squares algorithm is employed.

[0129] Next, the drift of model parameters will be described below. As shown in FIG. 6A and FIG. 6B, if a residual identifying error, which is caused by nonlinear characteristics such as friction characteristics of the throttle valve, exists after the model parameters have been converged to a certain extent, or if a disturbance whose average value is not zero is steadily applied, then residual identifying errors are accumulated, causing a drift of model parameters.

[0130] Since such a residual identifying error should not be reflected to the values of model parameters, a dead zone process is carried out using a dead zone function Fn1 as shown in FIG. 7A. Specifically, a corrected identifying error idenl(k) is calculated from the following equation (23), and a model parameter vector θ(k) is calculated using the corrected identifying error idenl(k). That is, the following equation (15a) is used instead of the above equation (15). In this manner, the requirement B3) can be satisfied.

idenl(k)=Fnl(ide(k))  (23)

θ(k)=θ(k−1)+KP(k)idenl(k)  (15a)

[0131] The dead zone function Fn1 is not limited to the function shown in FIG. 7A. A discontinuous dead zone function as shown in FIG. 7B or an incomplete dead zone function as shown in FIG. 7C may be used as the dead zone function Fnl. However, it is impossible to completely prevent the drift if the incomplete dead zone function is used.

[0132] The amplitude of the residual identifying error changes according to an amount of change in the throttle valve opening TH. In the present embodiment, a dead zone width parameter EIDNRLMT which defines the width of the dead zone shown in FIGS. 7A through 7C is set according to the square average value DDTHRSQA of an amount of change in the target throttle valve opening THR. Specifically, the dead zone width parameter EIDNRLMT is set such that it increases as the square average value DDTHRSQA increases. According to such setting of the dead zone width parameter EIDNRLMT, it is prevented to neglect an identifying error to be reflected to the values of the model parameters as the residual identifying error. In the following equation (24), DDTHR represents an amount of change in the target throttle valve opening THR, which is calculated from the following equation (25): $\begin{matrix} {{{DDTHRSQA}(k)} = {\frac{1}{n + 1}{\sum\limits_{i = 0}^{n}{{DDTHR}(i)}^{2}}}} & (24) \\ \begin{matrix} {{{DDTHR}(k)} = \quad {{{DTHR}(k)} - {{DTHR}\left( {k - 1} \right)}}} \\ {= \quad {{{THR}(k)} - {{THR}\left( {k - 1} \right)}}} \end{matrix} & (25) \end{matrix}$

[0133] Since the throttle valve opening deviation amount DTH is controlled to the target value DTHR by the adaptive sliding mode controller 21, the target value DTHR in the equation (25) may be changed to the throttle valve opening deviation amount DTH. In this case, an amount of change DDTH in the throttle valve opening deviation amount DTH may be calculated, and the dead zone width parameter EIDNRLMT may be set according to the square average value DDTHRSQA obtained by replacing DDTHR in the equation (24) with DDTH.

[0134] For further improving the robustness of the control system, it is effective to further stabilize the adaptive sliding mode controller 21. In the present embodiment, the elements a1′, a2′, b1′, and c1′ of the model parameter vector θ(k) calculated from the equation (15) are subjected to the limit process so that a corrected model parameter vector θL(k) (θL(k)^(T)=[a1, a2, b1, c1]) is calculated. The adaptive sliding mode controller 21 performs a sliding mode control using the corrected model parameter vector θL(k). The limit process will be described later in detail referring to the flowcharts.

[0135] Next, a method for calculating the predicted deviation amount PREDTH in the state predictor 23 will be described below.

[0136] First, matrixes A, B and vectors X(k), U(k) are defined according to the following equations (26) through (29). $\begin{matrix} {A = \begin{bmatrix} {a1} & {a2} \\ 1 & 0 \end{bmatrix}} & (26) \\ {B = \begin{bmatrix} {b1} & {c1} \\ 0 & 0 \end{bmatrix}} & (27) \\ {{X(k)} = \begin{bmatrix} {{DTH}(k)} \\ {{DTH}\left( {k - 1} \right)} \end{bmatrix}} & (28) \\ {{U(k)} = \begin{bmatrix} {{DUT}(k)} \\ 1 \end{bmatrix}} & (29) \end{matrix}$

[0137] By rewriting the equation (1) which defines the controlled object model, using the matrixes A, B and the vectors X(k), U(k), the following equation (30) is obtained.

X(k+1)=AX(k)+BU(k−d)  (30)

[0138] Determining X(k+d) from the equation (30), the following equation (31) is obtained. $\begin{matrix} {\left. {{X\left( {k + d} \right)} = {{A^{d}{X(k)}} + \left\lbrack {A^{d - 1}B\quad A^{d - 2}B\quad \cdots \quad {AB}\quad B} \right.}} \right\rbrack \begin{bmatrix} {U\left( {k - 1} \right)} \\ {U\left( {k - 2} \right)} \\ \vdots \\ {U\left( {k - d} \right)} \end{bmatrix}} & (31) \end{matrix}$

[0139] If matrixes A′ and B′ are defined by the following equations (32), (33), using the model parameters a1′, a2′, b1′, and c1′ which are not subjected to the limit process, a predicted vector XHAT(k+d) is given by the following equation (34). $\begin{matrix} {A^{\prime} = \begin{bmatrix} {a1}^{\prime} & {a2}^{\prime} \\ 1 & 0 \end{bmatrix}} & (32) \\ {B^{\prime} = \begin{bmatrix} {b1}^{\prime} & {c1}^{\prime} \\ 0 & 0 \end{bmatrix}} & (33) \\ {{{XHAT}\left( {k + d} \right)} = {{A^{\prime \quad d}{X(k)}} + {\left\lbrack {A^{{\prime \quad d} - 1}B^{\prime}\quad A^{{\prime \quad d} - 2}B^{\prime}\quad \cdots \quad A^{\prime}B^{\prime}\quad B^{\prime}} \right\rbrack \begin{bmatrix} {U\left( {k - 1} \right)} \\ {U\left( {k - 2} \right)} \\ \vdots \\ {U\left( {k - d} \right)} \end{bmatrix}}}} & (34) \end{matrix}$

[0140] The first-row element DTHHAT(k+d) of the predicted vector XHAT(k+d) corresponds to the predicted deviation amount PREDTH(k), and is given by the following equation (35). $\begin{matrix} \begin{matrix} {{{PREDTH}(k)} = \quad {{DTHHAT}\left( {k + d} \right)}} \\ {= \quad {{{\alpha 1} \times {{DTH}(k)}} + {{\alpha 2} \times {{DTH}\left( {k - 1} \right)}} +}} \\ {\quad {{{\beta 1} \times {{DUT}\left( {k - 1} \right)}} + {{\beta 2} \times {{DUT}\left( {k - 2} \right)}} + \ldots +}} \\ {\quad {{\beta \quad d \times {{DUT}\left( {k - d} \right)}} + {\gamma 1} + {\gamma 2} + \ldots + {\gamma \quad d}}} \end{matrix} & (35) \end{matrix}$

[0141] where α1 represents a first-row, first-column element of the matrix A′^(d), α2 represents a first-row, second-column element of the matrix A′^(d), βi represents a first-row, first-column element of the matrix A′^(d-i)B′, and γi represents a first-row, second-column element of the matrix A′^(d-i)B′.

[0142] By applying the predicted deviation amount PREDTH(k) calculated from the equation (35) to the equation (9), and replacing the target values DTHR(k+d+1), DTHR(k+d), and DTHR(k+d−1) respectively with DTHR(k), DTHR(k−1), and DTHR(k−2), the following equation (9a) is obtained. From the equation (9a), the equivalent control input Ueq(k) is calculated. $\begin{matrix} \begin{matrix} {{{DUT}(k)} = \quad {\frac{1}{b1}\left\{ {{\left( {1 - {a1} - {VPOLE}} \right){{PREDTH}(k)}} +} \right.}} \\ {\quad {{\left( {{VPOLE} - {a2}} \right){{PREDTH}\left( {k - 1} \right)}} -}} \\ {\quad {{c1} + {{DTHR}(k)} + {\left( {{VPOLE} - 1} \right){{DTHR}\left( {k - 1} \right)}} -}} \\ {\quad \left. {{VPOLE} \times {{DTHR}\left( {k - 2} \right)}} \right\}} \\ {= \quad {{Ueq}(k)}} \end{matrix} & \text{(9a)} \end{matrix}$

[0143] Using the predicted deviation amount PREDTH(k) calculated from the equation (35), a predicted switching function value σpre(k) is defined by the following equation (36). The reaching law input Urch(k) and the adaptive law input Uadp(k) are calculated respectively from the following equations (10a) and (11a).

σpre(k)=(PREDTH(k)−DTHR(k−1))+VPOLE(PREDTH(k−1)−DTHR(k−2))  (36) $\begin{matrix} {{{Urch}(k)} = {\frac{- F}{b1}\sigma \quad {{pre}(k)}}} & \text{(10a)} \\ {{{Uadp}(k)} = {\frac{- G}{b1}{\sum\limits_{i = 0}^{k}{\Delta \quad T\quad \sigma \quad {{pre}(i)}}}}} & \text{(11a)} \end{matrix}$

[0144] The model parameter c1′ is a parameter representing a deviation of the default opening THDEF and disturbance. Therefore, as shown in FIG. 8, the model parameter c1′ changes with disturbance, but can be regarded as substantially constant in a relatively short period. In the present embodiment, the model parameter c1′ is statistically processed, and the central value of its variations is calculated as a default opening deviation thdefadp. The default opening deviation thdefadp is used for calculating the throttle valve opening deviation amount DTH and the target value DTHR.

[0145] Generally, the method of least squares is known as a method of the statistic process. In the statistic process according to the method of least squares, all data, i.e., all identified parameters c1′, obtained in a certain period are stored in a memory and the stored data is subjected to a batch calculation of the statistic process at a certain timing. However, the batch calculation requires a memory having a large storage capacity for storing all data, and an increased amount of calculations are necessary because inverse matrix calculations are required.

[0146] Therefore, according to the present embodiment, the sequential method-of-least-squares algorithm for adaptive control which is indicated by the equations (15) through (21) is applied to the statistic process, and the central value of the least squares of the model parameter c1 is calculated as the default opening deviation thdefadp.

[0147] Specifically, in the equations (15) through (21), by replacing θ(k) and θ(k)^(T) with thdefadp, replacing ζ(k) and ζ(k)^(T) with “1”, replacing ide(k) with ecl(k), replacing KP(k) with KPTH(k), replacing P(k) with PTH(k), and replacing λ1 and λ2 respectively with λ1′ and λ2′, the following equations (37) through (40) are obtained. $\begin{matrix} {{{thdefadp}\left( {k + 1} \right)} = {{{thdefadp}(k)} + {{{KPTH}(k)}{{ec1}(k)}}}} & (37) \\ {{{KPTH}(k)} = \frac{{PTH}(k)}{1 + {{PTH}(k)}}} & (38) \\ {{{PTH}\left( {k + 1} \right)} = {\frac{1}{\lambda_{1}^{\prime}}\left( {1 - \frac{\lambda_{2}^{\prime}{{PTH}(k)}}{\lambda_{1}^{\prime} + {\lambda_{2}^{\prime}{{PTH}(k)}}}} \right){{PTH}(k)}}} & (39) \\ {{{ec1}(k)} = {{{c1}^{\prime}(k)} - {{thdefadp}(k)}}} & (40) \end{matrix}$

[0148] One of the four algorithms described above can be selected according to the setting of the coefficients λ1′ and λ2′. In the equation (39), the coefficient λ1′ is set to a given value other than 0 or 1, and the coefficient λ2′ is set to 1, thus employing the weighted method of least squares.

[0149] For the calculations of the equations (37) through (40), the values to be stored are thdefadp(k+1) and PTh(k+1) only, and no inverse matrix calculations are required. Therefore, by employing the sequential method-of-least-squares algorithm, the model parameter c1 can be statistically processed according to the method of least squares while overcoming the shortcomings of a general method of least squares.

[0150] The default opening deviation thdefadp obtained as a result of the statistic process is applied to the equations (2) and (3), and the throttle valve opening deviation amount DTH(k) and the target value DTHR(k) are calculated from the following equations (41) and (42) instead of the equations (2) and (3).

DTH(k)=TH(k)−THDEF+thdefadp  (41)

DTHR(k)=THR(k)−THDEF+thdefadp  (42)

[0151] Using the equations (41) and (42), even when the default opening THDEF is shifted from its designed value due to characteristic variations or aging of the hardware, the shift can be compensated to perform an accurate control process.

[0152] Operation processes executed by the CPU in the ECU 7 for realizing the functions of the adaptive sliding mode controller 21, the model parameter identifier 22, and the state predictor 23 will be described below.

[0153]FIG. 9 is a flowchart showing a process of the throttle valve opening control. The process is executed by the CPU in the ECU 7 in every predetermined period of time (e.g., 2 msec).

[0154] In step S11, a process of setting a state variable shown in FIG. 10 is performed. Calculations of the equations (41) and (42) are executed to determine the throttle valve opening deviation amount DTH(k) and the target value DTHR(k) (steps S21 and S22 in FIG. 10). The symbol (k) representing a current value may sometimes be omitted as shown in FIG. 10.

[0155] In step S12, a process of performing calculations of the model parameter identifier as shown in FIG. 11, i.e., a process of calculating the model parameter vector θ(k) from the equation (15a), is carried out. Further, the model parameter vector θ(k) is subjected to the limit process so that the corrected model parameter vector θL(k) is calculated.

[0156] In step S13, a process of performing calculations of the state predictor as shown in FIG. 21 is carried out to calculate the predicted deviation amount PREDTH(k).

[0157] Next, using the corrected model parameter vector θL(k) calculated in step S12, a process of calculating the control input Usl(k) as shown in FIG. 22 is carried out in step S14. Specifically, the equivalent control input Ueq, the reaching law input Urch(k), and the adaptive law input Uadp(k) are calculated, and the control input Usl(k) (=duty ratio DUT(k)) is calculated as a sum of these inputs Ueq(k), Urch(k), and Uadp(k).

[0158] In step S16, a process of stability determination of the sliding mode controller as shown in FIG. 29 is carried out. Specifically, the stability is determined based on a differential value of the Lyapunov function, and a stability determination flag FSMCSTAB is set. When the stability determination flag FSMCSTAB is set to “1”, this indicates that the adaptive sliding mode controller 21 is unstable.

[0159] If the stability determination flag FSMCSTAB is set to “1”, indicating that the adaptive sliding mode controller 21 is unstable, the switching function setting parameter VPOLE is set to a predetermined stabilizing value XPOLESTB (see steps S231 and S232 in FIG. 24), and the equivalent control input Ueq is set to “0”. That is, the control process by the adaptive sliding mode controller 21 is switched to a control process based on only the reaching law input Urch and the adaptive law input Uadp, to thereby stabilize the control (see steps S206 and S208 in FIG. 22).

[0160] Further, when the adaptive sliding mode controller 21 has become unstable, the equations for calculating the reaching law input Urch and the adaptive law input Uadp are changed. Specifically, the values of the reaching law control gain F and the adaptive law control gain G are changed to values for stabilizing the adaptive sliding mode controller 21, and the reaching law input Urch and the adaptive law input Uadp are calculated without using the model parameter b1 (see FIGS. 27 and 28). According to the above stabilizing process, it is possible to quickly terminate the unstable state of the adaptive sliding mode controller 21, and to bring the adaptive sliding mode controller 21 back to its stable state.

[0161] In step S17, a process of calculating the default opening deviation thdefadp as shown in FIG. 30 is performed to calculate the default opening deviation thdefadp.

[0162]FIG. 11 is a flowchart showing a process of performing calculations of the model parameter identifier 22.

[0163] In step S31, the gain coefficient vector KP(k) is calculated from the equation (20). Then, the estimated throttle valve opening deviation amount DTHHAT(k) is calculated from the equation (18) in step S32. In step S33, a process of calculating the identifying error idenl(k) as shown in FIG. 12 is carried out. The estimated throttle valve opening deviation amount DTHHAT(k) calculated in step S32 is applied to the equation (17) to calculate the identifying error ide(k). Further in step S32, the dead zone process using the function shown in FIG. 7A is carried out to calculate the corrected identifying error idenl.

[0164] In step S34, the model parameter vector θ(k) is calculated from the equation (15a). Then, the model parameter vector θ(k) is subjected to the stabilization process in step S35. That is, each of the model parameters is subjected to the limit process to calculate the corrected model parameter vector θL(k).

[0165]FIG. 12 is a flowchart showing a process of calculating the identifying error idenl(k) which is carried out in step S33 shown in FIG. 11.

[0166] In step S51, the identifying error ide(k) is calculated from the equation (17). Then, it is determined whether or not the value of a counter CNTIDST which is incremented in step S53 is greater than a predetermined value XCNTIDST that is set according to the dead time d of the controlled object (step S52). The predetermined value XCNTIDST is set, for example, to “3” according to a dead time d=2. Since the counter CNTIDST has an initial value of “0”, the process first goes to step S53, in which the counter CNTIDST is incremented by “1”. Then, the identifying error ide(k) is set to “0” in step S54, after which the process goes to step S55. Immediately after starting identifying the model parameter vector θ(k), no correct identifying error can be obtained by the equation (17). Therefore, the identifying error ide(k) is set to “0” according to steps S52 through S54, instead of using the calculated result of the equation (17).

[0167] If the answer to the step S52 is affirmative (YES), then the process immediately proceeds to step S55.

[0168] In step S55, the identifying error ide(k) is subjected to a low-pass filtering. Specifically, when identifying the model parameters of an controlled object which has low-pass characteristics, the identifying weight of the method-of-least-squares algorithm for the identifying error ide(k) has frequency characteristics as indicated by the solid line L1 in FIG. 13A. By the low-pass filtering of the identifying error ide(k), the frequency characteristics as indicated by the solid line L1 is changed to a frequency characteristics as indicated by the broken line L2 where the high-frequency components are attenuated. The reason for executing the low-pass filtering will be described below.

[0169] The frequency characteristics of the actual controlled object having low-pass characteristics and the controlled object model thereof are represented respectively by the solid lines L3 and L4 in FIG. 13B. Specifically, if the model parameters are identified by the model parameter identifier 22 with respect to the controlled object which has low-pass characteristics (characteristics of attenuating high-frequency components), the identified model parameters are largely affected by the high-frequency-rejection characteristics so that the gain of the controlled object model becomes lower than the actual characteristics in a low-frequency range. As a result, the sliding mode controller 21 excessively corrects the control input.

[0170] By changing the frequency characteristics of the weighting of the identifying algorithm to the characteristics indicated by the broken line L2 in FIG. 13A according to the low-pass filtering, the frequency characteristics of the controlled object are changed to frequency characteristics indicated by the broken line L5 in FIG. 13B. As a result, the frequency characteristics of the controlled object model is made to coincide with the actual frequency characteristics, or the low frequency gain of the controlled object model is corrected to a level which is slightly higher than the actual gain. Accordinly, it is possible to prevent the control input from being excessively corrected by the sliding mode controller 21, to thereby improve the robustness of the control system and further stabilize the control system.

[0171] The low-pass filtering is carried out by storing past values ide(k−i) of the identifying error (e.g., 10 past values for i=1 through 10) in a ring buffer, multiplying the past values by weighting coefficients, and adding the products of the past values and the weighting coefficients.

[0172] Since the identifying error ide(k) is calculated from the equations (17), (18), and (19), the same effect as described above can be obtained by performing the same low-pass filtering on the throttle valve opening deviation amount DTH(k) and the estimated throttle valve opening deviation amount DTHHAT(k), or by performing the same low-pass filtering on the throttle valve opening deviation amounts DTH(k−1), DTH(k−2) and the duty ratio DUT(k−d−1).

[0173] Referring back to FIG. 12, the dead zone process as shown in FIG. 14 is carried out in step S56. In step S61 shown in FIG. 14, “n” in the equation (24) is set, for example, to “5” to calculate the square average value DDTHRSQA of an amount of change of the target throttle valve opening THR. Then, an EIDNRLMT table shown in FIG. 15 is retrieved according to the square average value DDTHRSQA to calculate the dead zone width parameter EIDNRLMT (step S62).

[0174] In step S63, it is determined whether or not the identifying error ide(k) is greater than the dead zone width parameter EIDNRLMT. If ide(k) is greater than EIDNRLMT, the corrected identifying error idenl(k) is calculated from the following equation (43) in step S67.

idenl(k)=ide(k)−EIDNRLMT  (43)

[0175] If the answer to step S63 is negative (NO), it is determined whether or not the identifying error ide(k) is greater than the negative value of the dead zone width parameter EIDNRLMT with a minus sign (step S64).

[0176] If ide(k) is less than −EIDNRLMT, the corrected identifying error idenl(k) is calculated from the following equation (44) in step S65.

idenl(k)=ide(k)+EIDNRLMT  (44)

[0177] If the identifying error ide(k) is in the range between +EIDNRLMT and −EIDNRLMT, the corrected identifying error idenl(k) is set to “0” in step S66.

[0178]FIG. 16 is a flowchart showing a process of stabilizing the model parameter vector θ(k), which is carried out in step S35 shown in FIG. 11.

[0179] In step S71 shown in FIG. 16, flags FA1STAB, FA2STAB, FB1LMT, and FC1LMT used in this process are initialized to be set to “0”. In step S72, the limit process of the model parameters a1′ and a2′ shown in FIG. 17 is executed. In step S73, the limit process of the model parameter b1′ shown in FIG. 19 is executed. In step S74, the limit process of the model parameter c1′ shown in FIG. 20 is executed.

[0180]FIG. 17 is a flowchart showing the limit process of the model parameters a1′ and a2′, which is carried out in the step S72 shown in FIG. 16. FIG. 18 is a diagram illustrative of the process shown in FIG. 17, and will be referred to with FIG. 17.

[0181] In FIG. 18, combinations of the model parameters a1′ and a2′ which are required to be limited are indicated by “x” symbols, and the range of combinations of the model parameters a1′ and a2′ which are stable are indicated by a hatched region (hereinafter referred to as “stable region”). The limit process shown in FIG. 17 is a process of moving the combinations of the model parameters a1′ and a2′ which are in the outside of the stable region into the stable region at positions indicated by “◯” symbols.

[0182] In step S81, it is determined whether or not the model parameter a2′ is greater than or equal to a predetermined a2 lower limit value XIDA2L. The predetermined a2 lower limit value XIDA2L is set to a negative value greater than “−1”. Stable corrected model parameters a1 and a2 are obtained when setting the predetermined a2 lower limit value XIDA2L to “−1”. However, the predetermined a2 lower limit value XIDA2L is set to a negative value greater than “−1” because the matrix A defined by the equation (26) to the “n”th power may occasionally become unstable (which means that the model parameters a1′ and a2′ do not diverge, but oscillate).

[0183] If a2′ is less than XIDA2L in step S81, the corrected model parameter a2 is set to the lower limit value XIDA2L, and an a2 stabilizing flag FA2STAB is set to “1”. When the a2 stabilizing flag FA2STAB is set to “1”, this indicates that the corrected model parameter a2 is set to the lower limit value XIDA2L. In FIG. 18, the correction of the model parameter in a limit process P1 of steps S81 and S82 is indicated by the arrow lines with “P1”.

[0184] If the answer to the step S81 is affirmative (YES), i.e., if a2′ is greater than or equal to XIDA2L, the corrected model parameter a2 is set to the model parameter a2′ in step S83.

[0185] In steps S84 and S85, it is determined whether or not the model parameter a1′ is in a range defined by a predetermined a1 lower limit value XIDA1L and a predetermined a1 upper limit value XIDA1H. The predetermined a1 lower limit value XIDA1L is set to a value which is equal to or greater than “−2” and lower than “0”, and the predetermined a1 upper limit value XIDA1H is set to “2”, for example.

[0186] If the answers to steps S84 and S85 are affirmative (YES), i.e., if a1′ is greater than or equal to XIDA1L and less than or equal to XIDA1H, the corrected model parameter a1 is set to the model parameter a1′ in step S88.

[0187] If a1′ is less than XIDA1L in step S84, the corrected model parameter a1 is set to the lower limit value XIDA1L and an a1 stabilizing flag FA1STAB is set to “1” in step S86. If a1′ is greater than XIDA1H in step S85, the corrected model parameter a1 is set to the upper limit value XIDA1H and the a1 stabilizing flag FA1STAB is set to “1” in step S87. When the a1 stabilizing flag FA1STAB is set to “1”, this indicates that the corrected model parameter a1 is set to the lower limit value XIDA1L or the upper limit value XIDA1H. In FIG. 18, the correction of the model parameter in a limit process P2 of steps S84 through S87 is indicated by the arrow lines with “P2”.

[0188] In step S90, it is determined whether or not the sum of the absolute value of the corrected model parameter a1 and the corrected model parameter a2 is less than or equal to a predetermined stability determination value XA2STAB. The predetermined stability determination value XA2STAB is set to a value close to “1” but less than “1” (e.g., “0.99”).

[0189] Straight lines L1 and L2 shown in FIG. 18 satisfy the following equation (45).

a2+|a1|=XA2STAB  (45)

[0190] Therefore, in step S90, it is determined whether or not the combination of the corrected model parameters a1 and a2 is placed at a position on or lower than the straight lines L1 and L2 shown in FIG. 18. If the answer to step S90 is affirmative (YES), the limit process immediately ends, since the combination of the corrected model parameters a1 and a2 is in the stable region shown in FIG. 18.

[0191] If the answer to step S90 is negative (NO), it is determined whether or not the corrected model parameter a1 is less than or equal to a value obtained by subtracting the predetermined a2 lower limit value XIDA2L from the predetermined stability determination value XA2STAB in step S91 (since XIDA2L is less than “0”, XA2STAB−XIDA2L is greater than XA2STAB). If the corrected model parameter a1 is equal to or less than (XA2STAB−XIDA2L), the corrected model parameter a2 is set to (XA2STAB−|a1|) and the a2 stabilizing flag FA2STAB is set to “1” in step S92.

[0192] If the corrected model parameter a1 is greater than (XA2STAB−XIDA2L) in step S91, the corrected model parameter a1 is set to (XA2STAB−XIDA2L) in step S93. Further in step S93, the corrected model parameter a2 is set to the predetermined a2 lower limit value XIDA2L, and the a1 stabilizing flag FA1STAB and the a2 stabilizing flag FA2STAB are set to “1” in step S93.

[0193] In FIG. 18, the correction of the model parameter in a limit process P3 of steps S91 and S92 is indicated by the arrow lines with “P3”, and the correction of the model parameter in a limit process P4 in steps S91 and S93 is indicated by the arrow lines with “P4”.

[0194] As described above, the limit process shown in FIG. 17 is carried out to bring the model parameters a1′ and a2′ into the stable region shown in FIG. 18, thus calculating the corrected model parameters a1 and a2.

[0195]FIG. 19 is a flowchart showing a limit process of the model parameter b1′, which is carried out in step S73 shown in FIG. 16.

[0196] In steps S101 and S102, it is determined whether or not the model parameter b1′ is in a range defined by a predetermined b1 lower limit value XIDB1L and a predetermined b1 upper limit value XIDB1H. The predetermined b1 lower limit value XIDB1L is set to a positive value (e.g., “0.1”), and the predetermined b1 upper limit value XIDB1H is set to “1”, for example.

[0197] If the answers to steps S101 and S102 are affirmative (YES), i.e., if b1′ is greater than or equal to XIDB1L and less than or equal to XIDB1H, the corrected model parameter b1 is set to the model parameter b1′ in step S105.

[0198] If b1′ is less than XIDB1L in step S101, the corrected model parameter b1 is set to the lower limit value XIDB1L, and a b1 limiting flag FB1LMT is set to “1” in step S104. If b1′ is greater than XIDB1H in step S102, then the corrected model parameter b1 is set to the upper limit value XIDB1H, and the b1 limiting flag FB1LMT is set to “1” in step S103. When the b1 limiting flag FB1LMT is set to “1”, this indicates that the corrected model parameter b1 is set to the lower limit value XIDB1L or the upper limit value XIDB1H.

[0199]FIG. 20 is a flowchart showing a limit process of the model parameter c1′, which is carried out in step S74 shown in FIG. 16.

[0200] In steps S111 and S112, it is determined whether or not the model parameters c1′ is in a range defined by a predetermined c1 lower limit value XIDC1L and a predetermined c1 upper limit value XIDC1H. The predetermined c1 lower limit value XIDC1L is set to “−60”, for example, and the predetermined c1 upper limit value XIDC1H is set to “60”, for example.

[0201] If the answers to steps S111 and S112 are affirmative (YES), i.e., if c1′ is greater than or equal to XIDC1L and less than or equal to XIDC1H, the corrected model parameter c1 is set to the model parameter c1′ in step S115.

[0202] If c1′ is less than XIDC1L in step S111, the corrected model parameter c1 is set to the lower limit value XIDC1L, and a c1 limiting flag FC1LMT is set to “1” in step S114. If c1′ is greater than XIDC1H in step S112, the corrected model parameter c1 is set to the upper limit value XIDC1H, and the c1 limiting flag FC1LMT is set to “1” in step S113. When the c1 limiting flag FC1LMT is set to “1”, this indicates that the corrected model parameter c1 is set to the lower limit value XIDC1L or the upper limit value XIDC1H.

[0203]FIG. 21 is a flowchart showing a process of calculations of the state predictor, which is carried out in step S13 shown in FIG. 9.

[0204] In step S121, the matrix calculations are executed to calculate the matrix elements α1, α2, β1, β2, γ1 through γd in the equation (35).

[0205] In step S122, the predicted deviation amount PREDTH(k) is calculated from the equation (35).

[0206]FIG. 22 is a flowchart showing a process of calculation of the control input Usl (=DUT) applied to the throttle valve actuating device 10, which is carried out in step S14 shown in FIG. 9.

[0207] In step S201, a process of calculation of the predicted switching function value σpre, which is shown in FIG. 23, is executed. In step S202, a process of calculation of the integrated value of the predicted switching function value σpre, which is shown in FIG. 26, is executed. In step S203, the equivalent control input Ueq is calculated from the equation (9). In step S204, a process of calculation of the reaching law input Urch, which is shown in FIG. 27, is executed. In step S205, a process of calculation of the adaptive law input Uadp, which is shown in FIG. 28, is executed.

[0208] In step S206, it is determined whether or not the stability determination flag FSMCSTAB set in the process shown in FIG. 29 is “1”. When the stability determination flag FSMCSTAB is set to “1”, this indicates that the adaptive sliding mode controller 21 is unstable.

[0209] If FSMCSTAB is “0” in step S206, indicating that the adaptive sliding mode controller 21 is stable, the control inputs Ueq, Urch, and Uadp which are calculated in steps S203 through S205 are added, thereby calculating the control input Usl in step S207.

[0210] If FSMCSTAB is “1” in step S206, indicating that the adaptive sliding mode controller 21 is unstable, the sum of the reaching law input Urch and the adaptive law input Uadp is calculated as the control input Usl. In other words, the equivalent control input Ueq is not used for calculating the control input Usl, thus preventing the control system from becoming unstable.

[0211] In steps S209 and S210, it is determined whether or not the calculated control input Usl is in a range defined between a predetermined upper limit value XUSLH and a predetermined lower limit value XUSLL. If the control input Usl is in the range between XUSLH and XUSLL, the process immediately ends. If the control input Usl is less than or equal to the predetermined lower limit value XUSLL in step S209, the control input Usl is set to the predetermined lower limit value XUSLL in step S212. If the control input Usl is greater than or equal to the predetermined upper limit value XUSLH in step S210, the control input Usl is set to the predetermined upper limit value XUSLH in step S211.

[0212]FIG. 23 is a flowchart showing a process of calculating the predicted switching function value σpre, which is carried out in step S201 shown in FIG. 22.

[0213] In step S221, the process of calculating the switching function setting parameter VPOLE, which is shown in FIG. 24 is executed. Then, the predicted switching function value σpre(k) is calculated from the equation (36) in step S222.

[0214] In steps S223 and S224, it is determined whether or not the calculated predicted switching function value σpre(k) is in a range defined between a predetermined upper value XSGMH and a predetermined lower limit value XSGML. If the calculated predicted switching function value σpre(k) is in the range between XSGMH and XSGML, the process shown in FIG. 23 immediately ends. If the calculated predicted switching function value σpre(k) is less than or equal to the predetermined lower limit value XSGML in step S223, the calculated predicted switching function value σpre(k) is set to the predetermined lower limit value XSGML in step S225. If the calculated predicted switching function value σpre(k) is greater than or equal to the predetermined upper limit value XSGMH in step S224, the calculated predicted switching function value σpre(k) is set to the predetermined upper limit value XSGMH in step S226.

[0215]FIG. 24 is a flowchart showing a process of calculating the switching function setting parameter VPOLE, which is carried out in step S221 shown in FIG. 23.

[0216] In step S231, it is determined whether or not the stability determination flag FSMCSTAB is “1”. If FSMCSTAB is “1” in step S231, indicating that the adaptive sliding mode controller 21 is unstable, the switching function setting parameter VPOLE is set to a predetermined stabilizing value XPOLESTB in step S232. The predetermined stabilizing value XPOLESTB is set to a value which is greater than “−1” but very close to “−1” (e.g., “−0.999”).

[0217] If FSMCSTAB is “0”, indicating that the adaptive sliding mode controller 21 is stable, an amount of change DDTHR(k) in the target value DTHR(k) is calculated from the following equation (46) in step S233.

DDTHR(k)=DTHR(k)−DTHR(k−1)  (46)

[0218] In step S234, a VPOLE map is retrieved according to the throttle valve opening deviation amount DTH and the amount of change DDTHR calculated in step S233 to calculate the switching function setting parameter VPOLE. As shown in FIG. 25A, the VPOLE map is set so that the switching function setting parameter VPOLE increases when the throttle valve opening deviation amount DTH has a value in the vicinity of “0”, i.e., when the throttle valve opening TH is in the vicinity of the default opening THDEF, and the switching function setting parameter VPOLE has a substantially constant value regardless of changes of the throttle valve opening deviation amount DTH, when the throttle valve opening deviation amount DTH has values which are not in the vicinity of “0”. The VPOLE map is also set so that the switching function setting parameter VPOLE increases as the amount of change DDTHR in target value increases as indicated by the solid line in FIG. 25B, and the switching function setting parameter VPOLE increases as the amount of change DDTHR in the target value has a value in the vicinity of “0” as indicated by the broken line in FIG. 25B, when the throttle valve opening deviation amount DTH has a value in the vicinity of “0”.

[0219] Specifically, when the target value DTHR for the throttle valve opening changes greatly in the decreasing direction, the switching function setting parameter VPOLE is set to a relatively small value. This makes it possible to prevent the throttle valve 3 from colliding with the stopper for stopping the throttle valve 3 in the fully closed position. In the vicinity of the default opening THDEF, the switching function setting parameter VPOLE is set to a relatively large value, which improves the controllability in the vicinity of the default opening THDEF.

[0220] As shown in FIG. 25C, the VPOLE map may be set so that the switching function setting parameter VPOLE decreases when the throttle valve opening TH is in the vicinity of the fully closed opening or the fully open opening. Therefore, when the throttle valve opening TH is in the vicinity of the fully closed opening or the fully open opening, the speed for the throttle valve opening TH to follow up the target opening THR is reduced. As a result, collision of the throttle valve 3 with the stopper can more positively be avoided (the stopper also stops the throttle valve 3 in the fully open position).

[0221] In steps S235 and S236, it is determined whether or not the calculated switching function setting parameter VPOLE is in a range defined between a predetermined upper limit value XPOLEH and a predetermined lower limit XPOLEL. If the switching function setting parameter VPOLE is in the range between XPOLEH and XPOLEL, the process shown immediately ends. If the switching function setting parameter VPOLE is less than or equal to the predetermined lower limit value XPOLEL in step S236, the switching function setting parameter VPOLE is set to the predetermined lower limit value XPOLEL in step S238. If the switching function setting parameter VPOLE is greater than or equal to the predetermined upper limit value XPOLEH in step S235, the switching function setting parameter VPOLE is set to the predetermined upper limit value XPOLEH in step S237.

[0222]FIG. 26 is a flowchart showing a process of calculating an integrated value of σpre, SUMSIGMA, of the predicted switching function value σpre. This process is carried out in step S202 shown in FIG. 22. The integrated value SUMSIGMA is used for calculating the adaptive law input Uadp in the process shown in FIG. 28 which will be described later (see the equation (11a)).

[0223] In step S241, the integrated value SUMSIGMA is calculated from the following equation (47) where ΔT represents a calculation period.

SUMSIGMA(k)=SUMSIGMA(k−1)+σpre×ΔT  (47)

[0224] In steps S242 and S243, it is determined whether or not the calculated integrated value SUMSIGMA is in a range defined between a predetermined upper limit value XSUMSH and a predetermined lower limit value XSUMSL. If the integrated value SUMSIGMA is in the range between XSUMSH and XSUMSL, the process immediately ends. If the integrated value SUMSIGMA is less than or equal to the predetermined lower limit value XSUMSL in step S242, the integrated value SUMSIGMA is set to the predetermined lower limit value XSUMSL in step S244. If the integrated value SUMSIGMA is greater than or equal to the predetermined upper limit value XSUMSH in step S243, the integrated value SUMSIGMA is set to the predetermined upper limit value XSUMSH in step S245.

[0225]FIG. 27 is a flowchart showing a process of calculating the reaching law input Urch, which is carried out in step S204 shown in FIG. 22.

[0226] In step S261, it is determined whether or not the stability determination flag FSMCSTAB is “1”. If the stability determination flag FSMCSTAB is “0”, indicating that the adaptive sliding mode controller 21 is stable, the control gain F is set to a normal gain XKRCH in step S262, and the reaching law input Urch is calculated from the following equation (48), which is the same as the equation (10a), in step S263.

Urch=−F×σpre/b1  (48)

[0227] If the stability determination flag FSMCSTAB is “1”, indicating that the adaptive sliding mode controller 21 is unstable, the control gain F is set to a predetermined stabilizing gain XKRCHSTB in step S264, and the reaching law input Urch is calculated according to the following equation (49), which does not include the model parameter b1, in step S265.

Urch=−F×σpre  (49)

[0228] In steps S266 and S267, it is determined whether the calculated reaching law input Urch is in a range defined between a predetermined upper limit value XURCHH and a predetermined lower limit value XURCHL. If the reaching law input Urch is in the range between XURCHH and XURCHL, the process immediately ends. If the reaching law input Urch is less than or equal to the predetermined lower limit value XURCHL in step S266, the reaching law input Urch is set to the predetermined lower limit value XURCHL in step S268. If the reaching law input Urch is greater than or equal to the predetermined upper limit value XURCHH in step S267, the reaching law input Urch is set to the predetermined upper limit value XURCHH in step S269.

[0229] As described above, when the adaptive sliding mode controller 21 becomes unstable, the control gain F is set to the predetermined stabilizing gain XKRCHSTB, and the reaching law input Urch is calculated without using the model parameter b1, which brings the adaptive sliding mode controller 21 back to its stable state. When the identifying process carried out by the model parameter identifier 22 becomes unstable, the adaptive sliding mode controller 21 becomes unstable. Therefore, by using the equation (49) that does not include the model parameter b1 which has become unstable, the adaptive sliding mode controller 21 can be stabilized.

[0230]FIG. 28 is a flowchart showing a process of calculating the adaptive law input Uadp, which is carried out in step S205 shown in FIG. 22.

[0231] In step S271, it is determined whether or not the stability determination flag FSMCSTAB is “1”. If the stability determination flag FSMCSTAB is “0”, indicating that the adaptive sliding mode controller 21 is stable, the control gain G is set to a normal gain XKADP in step S272, and the adaptive law input Uadp is calculated from the following equation (50), which corresponds to the equation (11a), in step S273.

Uadp=−G×SUMSIGMA/b1  (50)

[0232] If the stability determination flag FSMCSTAB is “1”, indicating that the adaptive sliding mode controller 21 is unstable, the control gain G is set to a predetermined stabilizing gain XKADPSTB in step S274, and the adaptive law input Uadp is calculated according to the following equation (51), which does not include the model parameter b1, in step S275.

Uadp=−G×SUMSIGMA  (51)

[0233] As described above, when the adaptive sliding mode controller 21 becomes unstable, the control gain G is set to the predetermined stabilizing gain XKADPSTB, and the adaptive law input Uadp is calculated without using the model parameter b1, which brings the adaptive sliding mode controller 21 back to its stable state.

[0234]FIG. 29 is a flowchart showing a process of determining the stability of the sliding mode controller, which is carried out in step S16 shown in FIG. 9. In this process, the stability is determined based on a differential value of the Lyapunov function, and the stability determination flag FSMCSTAB is set according to the result of the stability determination.

[0235] In step S281, a switching function change amount Dσpre is calculated from the following equation (52). Then, a stability determining parameter SGMSTAB is calculated from the following equation (53) in step S282.

Dσpre=σpre(k)−σpre(k−1)  (52)

SGMSTAB=Dσpre×σpre(k)  (53)

[0236] In step S283, it is determined whether or not the stability determination parameter SGMSTAB is less than or equal to a stability determining threshold XSGMSTAB. If SGMSTAB is greater than XSGMSTAB, it is determined that the adaptive sliding mode controller 21 may possibly be unstable, and an unstability detecting counter CNTSMCST is incremented by “1” in step S285. If SGMSTAB is less than or equal to XSGMSTAB, the adaptive sliding mode controller 21 is determined to be stable, and the count of the unstability detecting counter CNTSMCST is not incremented but maintained in step S284.

[0237] In step S286, it is determined whether or not the value of the unstability detecting counter CNTSMCST is less than or equal to a predetermined count XSSTAB. If CNTSMCST is less than or equal to XSSTAB, the adaptive sliding mode controller 21 is determined to be stable, and a first determination flag FSMCSTAB1 is set to “0” in step S287. If CNTSMCST is greater than XSSTAB, the adaptive sliding mode controller 21 is determined to be unstable, and the first determination flag FSMCSTAB1 is set to “1” in step S288. The value of the unstability detecting counter CNTSMCST is initialized to “0”, when the ignition switch is turned on.

[0238] In step S289, a stability determining period counter CNTJUDST is decremented by “1”. It is determined whether or not the value of the stability determining period counter CNTJUDST is “0” in step S290. The value of the stability determining period counter CNTJUDST is initialized to a predetermined determining count XCJUDST, when the ignition switch is turned on. Initially, therefore, the answer to step S290 is negative (NO), and the process immediately goes to step S295.

[0239] If the count of the stability determining period counter CNTJUDST subsequently becomes “0”, the process goes from step S290 to step S291, in which it is determined whether or not the first determination flag FSMCSTAB1 is “1”. If the first determination flag FSMCSTAB1 is “0”, a second determination flag FSMCSTAB2 is set to “0” in step S293. If the first determination flag FSMCSTAB1 is “1”, the second determination flag FSMCSTAB2 is set to “1” in step S292.

[0240] In step S294, the value of the stability determining period counter CNTJUDST is set to the predetermined determining count XCJUDST, and the unstability detecting counter CNTSMCST is set to “0”. Thereafter, the process goes to step S295.

[0241] In step S295, the stability determination flag FSMCSTAB is set to the logical sum of the first determination flag FSMCSTAB1 and the second determination flag FSMCSTAB2. The second determination flag FSMCSTAB2 is maintained at “1” until the value of the stability determining period counter CNTJUDST becomes “0”, even if the answer to step S286 becomes affirmative (YES) and the first determination flag FSMCSTAB1 is set to “0”. Therefore, the stability determination flag FSMCSTAB is also maintained at “1” until the value of the stability determining period counter CNTJUDST becomes “0”.

[0242]FIG. 30 is a flowchart showing a process of calculating the default opening deviation thdefadp, which is carried out in step S17 shown in FIG. 9.

[0243] In step S251 shown FIG. 30, a gain coefficient KPTH(k) is calculated according to the following equation (54).

KPTH(k)=PTH(k−1)/(1+PTH(k−1))  (54)

[0244] where PTH(k−1) represents a gain parameter calculated in step S253 when the present process was carried out in the preceding cycle.

[0245] In step S252, the model parameter c1′ calculated in the process of calculations of the model parameter identifier as shown in FIG. 11 and the gain coefficient KPTH(k) calculated in step S251 are applied to the following equation (55) to calculate a default opening deviation thdefadp(k).

thdefadp(k)=thdefadp(k−1)+KPTH(k)×(c1′−thdefadp(k−1))  (55)

[0246] In step S253, a gain parameter PTH(k) is calculated from the following equation (56):

PTH(k)={1−PTH(k−1)}/(XDEFADPW+PTH(k−1))}×PTH(k−1)/XDEFADPW  (56)

[0247] The equation (56) is obtained by setting λ1′ and λ2′ in the equation (39) respectively to a predetermined value XDEFADP and “1”.

[0248] According to the process shown in FIG. 30, the model parameter c1′ is statistically processed by the sequential method-of-weighted-least-squares algorithm to calculate the default opening deviation thdefadp.

[0249] In the present embodiment, the throttle valve actuating device 10 and a portion of the ECU 7 (i.e., an output circuit for supplying an energizing current to the motor 6) correspond to a plant. The process shown in FIG. 22 corresponds to a sliding mode controller. The process shown in FIG. 11 corresponds to an identifying means. The process shown in FIG. 12 corresponds to an identifying error calculating means. The process shown in FIG. 14 corresponds to an identifying error correcting means. The process shown in FIG. 21 corresponds to a predicting means.

[0250] Second Embodiment

[0251] In the first embodiment described above, the controlled object model is defined by the equation (1) including the dead time d, and the predicted deviation amount PREDTH after the elapse of the dead time d is calculated with the state predictor 23, to thereby control the controlled object model which includes the dead time. Accordingly, it is necessary to execute calculations corresponding to the state predictor 23 in the CPU, and the amount of calculations executed by the CPU becomes large. In the second embodiment, in order to reduce the calculation load on the CPU, the controlled object model is defined by the following equation (1a) where the dead time d is set to “0”, and the modeling error caused by setting the dead time d to “0” is compensated by the robustness of the adaptive sliding mode control.

DTH(k+1)=a1×DTH(k)+a2×DTH(k−1)+b1×DUT(k)+c1  (1a)

[0252] In order to further reduce the calculation load on the CPU, the fixed gain algorithm is employed as the algorithm for identifying the model parameters.

[0253] For further stabilizing the control, another process instead of the dead zone process is employed as the process for preventing the drift of the model parameters.

[0254] The second embodiment will be described below primarily with respect to details which are different from those of the first embodiment. Other details except for what will be described below are identical to those of the first embodiment.

[0255]FIG. 31 is a functional block diagram of a throttle valve control system which is realized by the ECU 7. The throttle valve control system comprises an adaptive sliding mode controller 21 a, a model parameter identifier 22 a, a model parameter scheduler 25, and a target opening setting unit 24 for setting a target opening THR for the throttle valve 3 according to the accelerator pedal depression amount ACC.

[0256] The adaptive sliding mode controller 21 a is supplied with a detected throttle valve opening TH instead of the predicted deviation amount PREDTH. The adaptive sliding mode controller 21 a calculates a duty ratio DUT in order to make the throttle valve opening TH coincide with a target opening THR.

[0257] Using the adaptive sliding mode controller 21 a offers the same advantages as described above in the first embodiment, and achieves the robustness of the control system against the dead time of the controlled object. Therefore, it is possible to compensate for the modeling error that is caused by setting the dead time d to “0”.

[0258] The model parameter identifier 22 a calculates a corrected model parameter vector θL (θL^(T)=[a1, a2, b1, c1]) according to a method which is different from the method in the first embodiment, and supplies the calculated corrected model parameter vector θL to the adaptive sliding mode controller 21 a. More specifically, the model parameter identifier 22 a calculates a model parameter vector θ by correcting a reference model parameter vector θbase supplied from the model parameter scheduler 25, based on the throttle valve opening TH and the duty ratio DUT. The model parameter identifier 22 a then performs the limit process of the model parameter vector θ to calculate a corrected model parameter vector θL, and supplies the corrected model parameter vector θL to the adaptive sliding mode controller 21 a. In this manner, model parameters a1, a2, and b1 which are optimum for making the throttle valve opening TH follow up the target opening THR are obtained, and a model parameter c1 indicative of disturbance and a deviation of the default opening THDEF is obtained.

[0259] The model parameter scheduler 25 calculates a reference model parameter vector θbase (θbase^(T)=[a1base, a2base, b1base, c1base]) based on the throttle valve opening TH, and supplies the calculated reference model parameter vector θbase to the model parameter identifier 22 a.

[0260] In the present embodiment, since the controlled object model is defined by the above equation (1a), the adaptive sliding mode controller 21 a calculates the equivalent control input Ueq(k), the reaching law input Urch(k), and the adaptive law input Uadp(k) from the following equations (9b), (10b), and (11b) instead of the above equations (9a), (10a), and (11a). $\begin{matrix} \begin{matrix} {{{DUT}(k)} = \begin{matrix} {\frac{1}{b1}\left\{ {{\left( {1 - {a1} - {VPOLE}} \right){{DTH}(k)}} +} \right.} \\ {{\left( {{VPOLE} - {a2}} \right){{DTH}\left( {k - 1} \right)}} - {c1} + {{DTHR}\left( {k + 1} \right)} +} \\ \left. {{\left( {{VPOLE} - 1} \right){{DTHR}(k)}} - {{VPOLE} \times {{DTHR}\left( {k - 1} \right)}}} \right\} \end{matrix}} \\ {= {{Ueq}(k)}} \end{matrix} & \text{(9b)} \\ {{{Urch}(k)} = {\frac{- F}{b1}{\sigma (k)}}} & \text{(10b)} \\ {{{Uadp}(k)} = {\frac{- G}{b1}{\sum\limits_{i = 0}^{k}{\Delta \quad {{T\sigma}(i)}}}}} & {\text{(11b}\text{)}} \end{matrix}$

[0261] The equations (9b), (10b), and (11b) are obtained by setting the dead time d to “0” in the equations (9), (10), and (11).

[0262] The model parameter identifier 22 a calculates a model parameter vector of the controlled object model based on the input (DUT(k)) and output (TH(k)) of the controlled object, as described above. Specifically, the model parameter identifier 22 a calculates a model parameter vector θ(k) from the following equation (15).

θ(k)=θ(k−1)+KP(k)ide(k)  (15)

[0263] The identifying error ide(k) in the equation (15) is defined by the following equations (17), (18), and (19a). The gain coefficient vector KP(k) is defined by the following equation (20), and the square matrix P(k) in the equation (20) is calculated from the following equation (21).

ide(k)=DTH(k)−DTHHAT(k)  (17)

DTHHAT(k)=θ(k−1)^(T)ζ(k)  (18)

ζ(k)^(T) =[DTH(k−1), DTH(k−2), DUT(k−1), 1]  (19a) $\begin{matrix} {{{KP}(k)} = \frac{{P(k)}{\zeta (k)}}{{+ {\zeta^{T}(k)}}{P(k)}{\zeta (k)}}} & (20) \\ {{{P\left( {k + 1} \right)} = {\frac{1}{\lambda_{1}}\left( {- \frac{\lambda_{2}{P(k)}{\zeta (k)}{\zeta^{T}(k)}}{\lambda_{1} + {\lambda_{2}{\zeta^{T}(k)}{P(k)}{\zeta (k)}}}} \right){P(k)}}}\left( {:{{Identity}\quad {Matrix}}} \right)} & (21) \end{matrix}$

[0264] In the present embodiment, the following requirements B4 and B5 are required to be satisfied, in addition to the requirements B1 through B3 which should be satisfied like the first embodiment should be satisfied.

[0265] B1) Adaptation to Quasi-Static Dynamic Characteristic Changes and Hardware Characteristics Variations

[0266] “Quasi-static dynamic characteristics changes” mean slow-rate characteristics changes such as power supply voltage fluctuations or hardware degradations due to aging.

[0267] B2) Adaptation to High-Rate Dynamic Characteristics Changes

[0268] Specifically, this means adaptation to dynamic characteristics changes depending on changes in the throttle valve opening TH.

[0269] B3) Prevention of a Drift of Model Parameters

[0270] The drift, which is an excessive increase of the absolute values of model parameters, should be prevented. The drift of model parameters is caused by the effect of the identifying error, which should not be reflected to the model parameters, due to nonlinear characteristics of the controlled object.

[0271] B4) Matching with the Calculating Capability of the ECU

[0272] Specifically, the amount of calculations is required to be further reduced.

[0273] B5) The Stabilization of Model Parameters (Control Performance)

[0274] Specifically, variations of identified model parameters should be minimized.

[0275] In order to satisfy the requirement B4, the coefficients λ1 and λ2 are set respectively to “1” and “0”, to thereby employ the fixed gain algorithm. Accordingly, the square matrix P(k) is made constant, and the calculation of the equation (21) can be omitted. As a result, the amount of calculations can greatly be reduced.

[0276] Specifically, when the fixed gain algorithm is employed, the equation (20) is simplified into the following equation (20a) where P represents a square matrix having constants as diagonal elements. $\begin{matrix} {{{KP}(k)} = \frac{{P\zeta}(k)}{1 + {{\zeta^{T}(k)}{{P\zeta}(k)}}}} & \text{(20a)} \end{matrix}$

[0277] According to the algorithm thus simplified, the amount of calculations can be reduced. However, the identifying ability is also slightly reduced. Further, the equation (15) for calculating the model parameter vector θ(k) can be rewritten to the following equation (15b) and has an integral structure of the identifying error ide(k). Therefore, the identifying error ide(k) is likely to be integrated to the model parameters to cause the drift of the model parameters.

θ(k)=θ(0)+KP(1)ide(1)+KP(2)ide(2)+ . . . +KP(k)ide(k)  (15b)

[0278] where θ(0) represents an initial vector having elements of initial values of the model parameters.

[0279] In the present embodiment, in order to prevent the drift of the model parameters, the model parameter vector θ(k) is calculated from the following equation (15c) instead of the above equation (15b).

θ(k)=θ(0)+DELTA ^(k−1) ×KP(1)ide(1)+DELTA ^(k−2) ×KP(2)ide(2)+ . . . +DELTA×KP(k−1)ide(k−1)+KP(k)ide(k)  (15c)

[0280] where DELTA represents a forgetting coefficient vector having forgetting coefficients DELTAi (i=1 through 4) as elements, as indicated by the following equation.

DELTA=[DELTA1, DELTA2, DELTA3, DELTA4]

[0281] The forgetting coefficients DELTAi are set to a value between 0 and 1 (0<DELTAi<1) and have a function to gradually reduce the influence of past identifying errors. One of the coefficient DELTA3 which is relevant to the calculation of the model parameter b1, and the coefficient DELTA4 which is relevant to the calculation of the model parameter c1 is set to “1”. In other words, one of the forgetting coefficients DELTA3 and DELTA4 is made noneffective. By setting one or more of the elements of the forgetting coefficient vector DELTA to “1”, it is possible to prevent a steady-state deviation between the target value DTHR and the throttle valve opening deviation amount DTH from occurring. If both of the coefficients DELTA3 and DELTA4 are set to “1”, the effect of preventing the model parameters from drifting becomes insufficient. Accordingly, it is preferable to set only one of the coefficients DELTA3 or DELTA4 to “1”.

[0282] If the equation (15) is rewritten into a recursive form, the following equations (15d) and (15e) are obtained. A process of calculating the model parameter vector θ(k) from the equations (15d) and (15e) instead of the equation (15) is hereinafter referred to as a δ correcting method, and dθ(k) defined by the equation (15e) is referred to as “updating vector”.

θ(k)=θ(0)+dθ(k)  (15d)

dθ(k)=DELTA×dθ(k−1)+KP(k)ide(k)  (15e)

[0283] According to the algorithm using the δ correcting method, the drift preventing effect which satisfies the requirement B3 and the model parameter stabilizing effect which satisfies the requirement B5 are obtained. Specifically, the initial vector θ(0) is maintained at all times, and the values which can be taken by the elements of the updating vector dθ(k) are limited by the effect of the coefficient vector DELTA. Therefore, the model parameters can be stabilized in the vicinity of their initial values.

[0284] Furthermore, since the model parameters are calculated while adjusting the updating vector dθ(k) according to the identifying process based on the input and output data of the actual controlled object, the model parameters matching the actual controlled object can be obtained, and hence the above requirement B1 is satisfied.

[0285] In order to satisfy the requirement B2, the model parameter vector θ(k) is calculated from the following equation (15f) which uses the reference model parameter vector θbase, instead of the initial vector θ(0) in the equation (15d).

θ(k)=θbase+dθ(k)  (15f)

[0286] The reference model parameter vector θbase is set according to the throttle valve opening deviation amount DTH (TH−THDEF) by the model parameter scheduler 25. Consequently, the reference model parameter vector θbase can be adapted to changes in the dynamic characteristics which correspond to changes in the throttle valve opening TH, and hence the above requirement B2 is satisfied.

[0287] As described above, according to the present embodiment, the amount of calculations by the ECU is reduced by employing the fixed gain algorithm (the requirement B4). The adaptation to quasi-static dynamic characteristics changes and hardware characteristics variations (the requirement B1), the stabilization of the model parameters (control performance) (the requirement B5), and the prevention of the drift of the model parameter (the requirement B3) are achieved by employing the algorithm using the δ correcting method. Further, the adaptation to dynamic characteristics changes corresponding to changes in the throttle opening TH is achieved by employing the model parameter scheduler 25 (the requirement B2).

[0288] The elements a1′, a2′, b1′, and c1′ of the model parameter vector θ(k) calculated from the equation (15f) are subjected to the limit process so that the corrected model parameter vector θL(k) (θL(k)^(T)=[a1, a2, b1, c1]) is calculated like the first embodiment.

[0289] In addition, the model parameter c1′ is statistically processed, and the central value of its variations is calculated as the default opening deviation thdefadp and used to calculate the throttle valve opening deviation amount DTH and the target value DTHR from the equations (41) and (42) like the first embodiment.

DTH(k)=TH(k)−THDEF+thdefadp  (41)

DTHR(k)=THR(k)−THDEF+thdefadp  (42)

[0290] Operations of the CPU of the ECU 7 for realizing the functions of the adaptive sliding mode controller 21 a, the model parameter identifier 22 a, and the model parameter scheduler 25 will be described below.

[0291]FIG. 32 is a flowchart showing a throttle valve opening control process. The throttle valve opening control process differs from the throttle valve opening control process shown in FIG. 9 in that step S13 (the calculations of the state predictor) in the latter process is omitted, and steps S12, S14, and S16 in the latter process are changed respectively to steps S12 a, S14 a, and S16 a.

[0292] In step S12 a, a process of performing calculations of the model parameter identifier as shown in FIG. 33. Specifically, a process of calculating the model parameter vector θ(k) from the equation (15f) is carried out, and the model parameter vector θ(k) is subjected to the limit process so that the corrected model parameter vector θL(k) is calculated.

[0293] In step S14 a, the control input Usl(k) shown in FIG. 36 is calculated using the corrected model parameter vector θL(k). Specifically, the equivalent control input Ueq, the reaching law input Urch(k), and the adaptive law input Uadp(k) are calculated, and the sum of these inputs is calculated as the control input Usl(k) (duty ratio DUT(k)).

[0294] In step S16 a, a process of determining the stability of the sliding mode controller as shown in FIG. 41 is carried out. Specifically, the stability of the sliding mode controller is determined using the switching function value σ instead of the predicted switching function value σpre, and the stability determination flag FSMCSTAB is set. The process performed when the stability determination flag FSMCSTAB is set to “1” is the same as that the first embodiment.

[0295]FIG. 33 is a flowchart showing a process of calculations of the model parameter identifier 22 a. The process shown in FIG. 33 is different from the process of calculations of the model parameter identifier as shown in FIG. 11 in that steps S31 through S34 in the process shown in FIG. 11 are changed to steps S31 a through S34 a, and steps S33 b and S33 c are added.

[0296] In step S31 a, the gain coefficient vector KP(k) is calculated from the equation (20a). Then, the estimated throttle valve opening deviation amount DTHHAT(k) is calculated from the equations (18) and (19a) in step S32 a.

[0297] In step S33 a, a process of calculating ide(k) as shown in FIG. 35 is carried out to calculate the identifying error ide(k). In step S33 b, the updating vector dθ(k) is calculated from the equation (15e). Then, a θbase table shown in FIG. 34 is retrieved according to the throttle valve opening deviation amount DTH to calculate the reference model parameter vector θbase in step S33 c. In the θbase table, reference model parameters a1base, a2base, and b1base are set. When the throttle valve opening deviation amount DTH has a value close to “0” (i.e., when the throttle opening TH is in the vicinity of the default opening THDEF), the reference model parameters a1base and b1base decrease and the reference model parameter a2base increases. The reference model parameter c1base is set to “0”.

[0298] In step S34 a, the model parameter vector θ(k) is calculated from the equation (15f). Then, the process of stabilizing the model parameter vector θ(k) is executed like the first embodiment (step S35). That is, the model parameters are subjected to the limit process so that the corrected model parameter vector θL(k) is calculated.

[0299]FIG. 35 is a flowchart showing a process of calculating the identifying error ide(k) which is carried out in step S33 a shown in FIG. 33. The process shown in FIG. 35 is different from the process shown in FIG. 12 in that the dead zone process in step S56 shown in FIG. 12 is omitted and step S51 shown in FIG. 12 is changed to step S51 a. In the present embodiment, the dead zone process is not performed, since the drift of the model parameters is prevented by the δ correcting method.

[0300] In step S51 a, the estimated throttle valve opening deviation amount DTHHAT(k) is calculated from the equations (18) and (19a), and the identifying error ide(k) is calculated using the estimated throttle valve opening deviation amount DTHHAT(k).

[0301] In the present embodiment, the predetermined value XCNTIDST in step S52 is set to “2”, for example, because the dead time d of the controlled object model is set to “0”.

[0302]FIG. 36 is a flowchart showing a process of calculating the control input Usl (DUT) applied to the throttle valve actuating device 10, which is carried out in step S14 a shown in FIG. 32. The process shown in FIG. 36 is different from the process shown in FIG. 22 in that steps S201 through S205 shown in FIG. 22 are changed respectively to steps S201 a through S205 a.

[0303] In step S201 a, the switching function value σ is calculated as shown in FIG. 37. In step S202 a, the integrated value of the switching function value σ is calculated as shown in FIG. 38. In step S203 a, the equivalent control input Ueq is calculated from the equation (9b). In step S204 a, the reaching law input Urch is calculated as shown in FIG. 39. In step S205 a, the adaptive law input Uadp is calculated as shown in FIG. 40.

[0304]FIG. 37 is a flowchart showing a process of calculating the switching function value σ which is carried out in step S201 a shown in FIG. 36. The process shown in FIG. 37 is different from the process shown in FIG. 23 in that steps S222 through S226 shown in FIG. 23 are changed respectively to steps S222 a through S226 a.

[0305] In step S222 a, the switching function value σ(k) is calculated from the equation (5). Steps S223 a through S226 a are the steps obtained by replacing “σpre” in steps S223 through S226 shown in FIG. 23 with “σ”. Accordingly, the switching function value σ(k) is subjected to the limit process in the same manner as the process shown in FIG. 23.

[0306]FIG. 38 is a flowchart showing a process of calculating an integrated value SUMSIGMAa of the switching function value σ which is carried out in step S202 a shown in FIG. 36. The process shown in FIG. 38 is different from the process shown in FIG. 26 in that steps S241 through S245 shown in FIG. 26 are changed respectively to steps S241 a through S245 a. The integrated value SUMSIGMAa is used for the calculation of the adaptive law input Uadp in a process shown in FIG. 40 described below (see the equation (11b)).

[0307] In step S241 a, the integrated value SUMSIGMAa is calculated according to the following equation (47a):

SUMSIGMAa(k)=SUMSIGMAa(k−1)+σ×ΔT  (47a)

[0308] In steps S242 a through S245 a, the calculated integrated value SUMSIGMAa is subjected to the limit process in the same manner as the process shown in FIG. 26.

[0309]FIG. 39 is a flowchart showing a process of calculating the reaching law input Urch, which is carried out in step S204 a shown in FIG. 36. The process shown in FIG. 39 is different from the process shown in FIG. 27 in that steps S263 and S265 shown in FIG. 27 are changed respectively to steps S263 a and S265 a.

[0310] In the present embodiment, using the switching function value σ instead of the predicted switching function value σpre, the reaching law input Urch is calculated in step S263 a, when the adaptive sliding mode controller 21 a is stable, and the reaching law input Urch is calculated in step S265 a when the adaptive sliding mode controller 21 a is unstable.

[0311]FIG. 40 is a flowchart showing a process of calculating the adaptive law input Uadp which is carried out in step S205 a shown in FIG. 36. The process shown in FIG. 40 is different from the process shown in FIG. 28 in that steps S273 and S275 shown in FIG. 28 are changed respectively to steps S273 a and S275 a.

[0312] In the present embodiment, using the integrated value SUMSIGMAa of the switching function value σ, the adaptive law input Uadp is calculated in step S273 a, when the adaptive sliding mode controller 21 a is stable, and the adaptive law input Uadp is calculated in step S275 a when the adaptive sliding mode controller 21 a is unstable.

[0313]FIG. 41 is a flowchart showing a process of determining the stability of the sliding mode controller which is carried out in step S16 a shown in FIG. 32. The process shown in FIG. 41 is different from the process shown in FIG. 29 in that steps S281 and S282 shown in FIG. 29 are changed respectively to steps S281 a and S282 a.

[0314] In step S281 a, the switching function change amount Dσ is calculated according to the following equation (52a). The stability determining parameter SGMSTAB is calculated according to the following equation (53a) in step S282 a. That is, the stability is determined on the basis of the switching function value σ instead of the predicted switching function value σpre.

Dσ=σ(k)−σ(k−1)  (52a)

SGMSTAB=Dσ×σ(k)  (53a)

[0315] In the present embodiment, the throttle valve actuating device 10 and a portion of the ECU 7, i.e., an output circuit for supplying an energizing current to the motor 6, correspond to a plant. The process shown in FIG. 36 corresponds to a sliding mode controller. The process shown in FIG. 33 corresponds to an identifying means. The process shown in FIG. 35 corresponds to an identifying error calculating means. The process shown in FIG. 35 corresponds to an identifying error correcting means. Step S33 b shown FIG. 33 corresponds to an updating vector calculating means and an updating vector correcting means.

[0316] Third Embodiment

[0317]FIG. 42 is a block diagram showing a control system according to the third embodiment of the present invention.

[0318] As shown in FIG. 42, the control system comprises a plant 101 as a controlled object, a pH sensor 102 for detecting a pH of a mixed liquid which is an output from the plant, a subtractor 103 for subtracting a first reference value V1BASE from an output V1OUT from the pH sensor 102, a target value generator 104 for generating a control target value V1TARGET, a control quantity determining unit 105 for determining a first control quantity U1, and an adder 106 for adding the first control quantity U1 and a second reference value V2BASE and outputting a second control quantity U2.

[0319] The subtractor 103, the target value generator 104, the control quantity determining unit 105, and the adder 106 are specifically implemented by an electronic control unit having a CPU, a memory, and an input/output circuit.

[0320] The plant 101 includes a flow rate control valve 111 for controlling the flow rate of an alkaline liquid according to the second control quantity U2, and a stirrer 112 for stirring an alkaline liquid supplied via the flow rate control valve 111 and an acid liquid. The plant 101 stirs the alkaline liquid and the acid liquid and outputs a mixed liquid having a desired pH value.

[0321] The control quantity determining unit 105 comprises an identifier 121 for identifying a model parameter vector of a controlled object model which is obtained by modeling the plant 101, an adaptive sliding mode controller 122, and a predictor 123. The identifier 121, the adaptive sliding mode controller 122, and the predictor 123 correspond respectively to the model parameter identifier 22, the adaptive sliding mode controller 21, and the state predictor 23 according to the first embodiment, and have the same functions as those of the model parameter identifier 22, the adaptive sliding mode controller 21, and the state predictor 23.

[0322] The relations between the components and parameters of the third embodiment and the components and parameters of the first embodiment will be described below.

[0323] The pH sensor 102 corresponds to the throttle valve opening sensor 8, and the output VIOUT of the pH sensor 102 corresponds to the throttle valve opening TH. The first target value V1BASE corresponds to the default opening THDEF. In the present preferred embodiment, the first target value V1BASE is set to a pH value corresponding to the neutral value. Therefore, a deviation amount DV1 corresponds to the throttle valve opening deviation amount DTH. The target value generator 104 corresponds to the target opening setting unit 24, and the control target value V1TARGET corresponds to the target value DTHR of the throttle valve opening deviation amount. In the first embodiment, the function of the subtractor 103 is included in the model parameter identifier 22 and the state predictor 23.

[0324] The second reference value V2BASE is added to bias the central value of the first control quantity U1 which is the output of the adaptive sliding mode controller 122. In the first embodiment, there is no component corresponding to the adder 106, and hence the second reference value V2BASE substantially equals “0” (i.e., U1=U2=Usl). In the present embodiment, the second reference value V2BASE is set to such a value that the opening of the flow rate control valve 111 is 50%, for example.

[0325] The flow rate control valve 111 corresponds to a switching element (which is included in the output circuit of the ECU 7, but not shown in the drawings and not explained the description) that is turned on and off by a pulse signal having the duty ratio DUT. The alkaline liquid corresponds to the supply voltage. An output flow rate V2 of the flow rate control valve 111 corresponds to an energizing current of the motor 6. The stirrer 112 corresponds to the motor 6 and the valve body of the throttle valve 3. The acid liquid corresponds to the intake pipe negative pressure acting on the valve body of the throttle valve 3 and the energizing forces of the return spring 4 and the resilient member 5. A pH value V1 of the mixed liquid outputted from the stirrer 112 corresponds to the actual throttle valve opening.

[0326] Because of the above relations, the plant 101 can be modeled in the same manner as the first embodiment, and the same control process as the control process according to the first embodiment can be applied to the plant 101. Specifically, the identifier 121 performs the same operation as that of the first embodiment to calculate the corrected model parameter vector θL, based on the first control quantity U1 and the deviation amount DV1. The predictor 123 performs the same operation as that of the first embodiment to calculate the predicted deviation PREDV1, based on the first control quantity U1, the deviation amount DV1, and the corrected model parameter vector θL. The adaptive sliding mode controller 122 performs the same operation as that of the first embodiment to calculate the first control quantity U1 in order to make the predicted deviation PREDV1 coincide with the control target value V1TARGET, based on the predicted deviation amount PREDV1 and the corrected model parameter vector θL. Therefore, the output V1 of the plant 101 can be made to coincide with a desired pH value by setting the control target value V1TARGET to a desired relative pH value (a desired deviation amount from the first reference value V1BASE).

[0327] In the present embodiment, the identifier 121 corresponds to an identifying means, and includes an identifying error calculating means and an identifying error correcting means. The predictor 123 corresponds to a predicting means.

[0328] Modification of Third Embodiment

[0329]FIG. 43 shows a modification of the control system shown in FIG. 42. In the modification, a plant 101 a, instead of the plant 101 shown in FIG. 42, is a controlled object. The plant 101 a is constituted by adding a flow rate sensor 113 and a feedback controller 114 to the plant 101 shown in FIG. 42. The flow rate sensor 113 detects an output flow rate V1 of the flow control valve 111, and the feedback controller 114 controls the flow control valve 111 to make an output V2OUT of the flow rate sensor 113 coincide with a flow rate corresponding to the second control quantity U2.

[0330] The modeling and the control process which are the same as those of the third embodiment are also applicable to the plant including the local feedback loop as shown in FIG. 43.

[0331] Since the circuit for energizing the motor in the first embodiment is already known, this circuit has not been described in detail. The circuit for energizing the motor may include a current sensor for detecting an output current of the switching element that is turned on and off, and a feedback control process may be carried out to make a detected current value ID coincide with a current value IR corresponding to the control quantity Usl. The present modification corresponds to a structure where the above circuit arrangement is applied to the first embodiment.

[0332] Fourth Embodiment

[0333]FIG. 44 is a block diagram of a control system according to the fourth embodiment of the present invention. The control system shown in FIG. 44 corresponds to the control system according to the second embodiment, and is similar to the control system shown in FIG. 42 except that the control quantity determining unit 105 shown in FIG. 42 is replaced with a control quantity determining unit 105 a. The details of the control system shown in FIG. 44 which will not be described below are identical to those of the control system according to the third embodiment.

[0334] The control quantity determining unit 105 a comprises an identifier 121 a, an adaptive sliding mode controller 122 a, and a parameter scheduler 124.

[0335] The identifier 121 a, the adaptive sliding mode controller 122 a, and the parameter scheduler 124 correspond respectively to, and have the same functions as, the model parameter identifier 22 a, the adaptive sliding mode controller 21 a, and the model parameter scheduler 25 according to the second embodiment.

[0336] The parameter scheduler 124 performs the same operation as that of the second embodiment to calculate the reference model parameter vector θbase, based on the deviation amount DV1. The identifier 121 a performs the same operation as that of the second embodiment to calculate the corrected model parameter vector θL based on the first control quantity U1, the deviation amount DV1, and the reference model parameter vector θbase. The adaptive sliding mode controller 122 a performs the same operation as that of the second embodiment to calculate the first control quantity U1 based on the deviation amount DV1 and the corrected model parameter vector θL, to make the deviation amount DV1 coincide with the control target value V1TARGET. Therefore, the output V1 of the plant 101 can be made to coincide with a desired pH value by setting the control target value V1TARGET to a desired relative pH value (a deviation amount from the first reference value V1BASE).

[0337] In the present embodiment, the identifier 121 a corresponds to an identifying means, and includes an identifying error calculating means, an updating vector calculating means, and an updating vector correcting means.

[0338] Modification of Fourth Embodiment

[0339]FIG. 45 shows a modification of the control system shown in FIG. 44. In the modification, a plant 101 a, instead of the plant 101 shown in FIG. 44, is a controlled object. The plant 101 a is identical to the plant 101 a shown in FIG. 43.

[0340] The modeling and the control process which are the same as those of the fourth embodiment are also applicable to the plant including the local feedback loop as shown in FIG. 45.

[0341] Other Embodiments

[0342] The δ correcting method may be replaced with a ε correcting method described below, as a method of calculating the identifying error ide(k) of the model parameters. Specifically, the following equation (15g) may be used instead of the equation (15c) to calculate the model parameter vector θ(k).

θ(k)=EPS ^(k)θ(0)+EPS ^(k−1) ×KP(1)ide(1)+EPS ^(k−2) ×KP(2)ide(2)+ . . . +EPS×KP(k−1)ide(k−1)+KP(k)ide(k)  (15g)

[0343] where EPS represents a forgetting coefficient vector having forgetting coefficients EPSi (i=1 through 4) as its elements, as indicated by the following equation.

EPS=[EPS1, EPS2, EPS3, EPS4]

[0344] Like the forgetting coefficients DELTAi, the forgetting coefficients EPS1, EPS2, and EPS4 are set to a value between “0” and “1” (0<EPSi<1) and have a function to gradually reduce the influence of past identifying errors.

[0345] In the ε correcting method, the coefficient EPS3 which is relevant to the calculation of the model parameter b1 must be set to “1” for the following reasons. In the ε correcting method, the all values of the model parameters becomes closer to zero, as the identifying error ide(k) becomes less. Since the model parameter b1 is applied to the denominator of the equations (9b), (10b), and (11b), the input Usl applied to the controlled object diverges as the model parameter b1 becomes closer to “0”.

[0346] The equation (15g) is different from the equation (15c) in that the initial vector θ(0) is also multiplied by the forgetting coefficient vector EPS.

[0347] If the equation (15g) is rewritten into a recursive form, the following equation (15h) is obtained. The method of calculating the model parameter vector θ(k) from the equation (15h) instead of the equation (15) is referred to as the ε correcting method.

θ(k)=EPS×θ(k−1)+KP(k)ide(k)  (15h)

[0348] The ε correcting method also has a function to gradually reduce the influence of past identifying errors eid. Accordingly, the drift of the model parameters are prevented by the ε correcting method.

[0349] In the second embodiment, the drift of the model parameters is prevented by the δ correcting method. However, like the first embodiment, the corrected identifying error idenl(k) may be calculated according to the dead zone process (FIG. 14), and the model parameter vector θ(k) may be calculated using the corrected identifying error idenl(k).

[0350] In the first embodiment, the dead zone process may be replaced with the δ correcting method or the ε correcting method.

[0351] Although certain preferred embodiments of the present invention have been shown and described in detail, it should be understood that various changes and modifications may be made therein without departing from the scope of the appended claims. 

What is claimed is:
 1. A control system for a plant, comprising: identifying means for identifying a model parameter vector of a controlled object model which is obtained by modeling said plant, based on an input and an output of said plant; and a sliding mode controller for controlling said plant using the model parameter vector identified by said identifying means; said identifying means comprising: identifying error calculating means for calculating an identifying error of the model parameter vector; updating vector calculating means for calculating an updating vector according to said identifying error; and updating vector correcting means for correcting said updating vector by multiplying a past value of at least one element of said updating vector by a predetermined value which is greater than 0 and less than 1; wherein said identifying means calculates the model parameter vector by adding the corrected updating vector to a reference vector of the model parameter vector.
 2. A control system according to claim 1, wherein said updating vector correcting means does not multiply one of an element of the updating vector which is relevant to the input of said plant and an element of the updating vector which is irrelevant to the input and the output of said plant, by the predetermined value.
 3. A control system according to claim 1, wherein said updating vector correcting means multiplies at least one element of the reference vector by the predetermined value.
 4. A control system according to claim 1, wherein said identifying means identifies the model parameter vector using a fixed gain algorithm.
 5. A control system according to claim 1, wherein said identifying error calculating means performs a low-pass filtering of the identifying error and outputs the identifying error after the low-pass filtering.
 6. A control system according to claim 1, further comprising predicting means for calculating a predicted value of the output of said plant, wherein said sliding mode controller uses the calculated predicted value.
 7. A control system according to claim 6, wherein said predicting means calculates the predicted value using the model parameter vector identified by said identifying means.
 8. A control system according to claim 1, wherein a control input applied from said sliding mode controller to said plant includes an adaptive law input.
 9. A control system according to claim 1, wherein said plant includes a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating said throttle valve, and said sliding mode controller calculates a parameter for determining a control input to be applied to said throttle valve actuating device to make the opening of said throttle valve coincide with a target opening.
 10. A control system for a plant, comprising: identifying means for identifying a model parameter vector of a controlled object model which is obtained by modeling said plant, based on an input and an output of said plant; and a sliding mode controller for controlling said plant using the model parameter vector identified by said identifying means; said identifying means comprising: identifying error calculating means for calculating an identifying error of the model parameter vector; and identifying error correcting means for correcting the identifying error in a decreasing direction, if the identifying error is in a predetermined range; wherein said identifying means calculates the model parameter vector using the identifying error corrected by said identifying error correcting means.
 11. A control system according to claim 10, wherein said identifying error correcting means sets the identifying error to “0”, if the identifying error is in the predetermined range.
 12. A control system according to claim 10, wherein the predetermined range is set according to an amount of change in a control target value or an amount of change in the output of said plant.
 13. A control system according to claim 10, further comprising predicting means for calculating a predicted value of the output of said plant, wherein said sliding mode controller uses the calculated predicted value.
 14. A control system according to claim 13, wherein said predicting means calculates the predicted value using the model parameter vector identified by said identifying means.
 15. A control system according to claim 10, wherein said plant includes a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating said throttle valve, and said sliding mode controller calculates a parameter for determining a control input to be applied to said throttle valve actuating device to make the opening of said throttle valve coincide with a target opening.
 16. A control method for a plant, comprising the steps of: a) identifying a model parameter vector of a controlled object model which is obtained by modeling said plant, based on an input and an output of said plant; and b) controlling said plant with a sliding mode control using the identified model parameter vector; said step a) of identifying the model parameter vector, comprising the steps of: i) calculating an identifying error of the model parameter vector; ii) calculating an updating vector according to said identifying error; and iii) correcting the updating vector by multiplying a past value of at least one element of the updating vector by a predetermined value which is greater than 0 and less than 1; wherein the model parameter vector is calculated by adding the corrected updating vector to a reference vector of the model parameter vector.
 17. A control method according to claim 16, wherein one of an element of the updating vector which is relevant to the input of said plant and an element of the updating vector which is irrelevant to the input and the output of said plant, is not multiplied by the predetermined value.
 18. A control method according to claim 16, wherein at least one element of the reference vector is multiplied by the predetermined value.
 19. A control method according to claim 16, wherein the model parameter vector is identified using a fixed gain algorithm.
 20. A control method according to claim 16, wherein a low-pass filtering of the identifying error is performed and the identifying error after the low-pass filtering is used in the calculation of the updating vector.
 21. A control method according to claim 16, further comprising the step of calculating a predicted value of the output of said plant, wherein the calculated predicted value is used in controlling said plant with the sliding mode control.
 22. A control method according to claim 21, wherein the predicted value is calculated using the identified model parameter vector.
 23. A control method according to claim 16, wherein a control input applied to said plant in said step b) includes an adaptive law input.
 24. A control method according to claim 16, wherein said plant includes a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating said throttle valve, and a parameter for determining a control input to be applied to said throttle valve actuating device is calculated in said step b) to make the opening of said throttle valve coincide with a target opening.
 25. A control method for a plant, comprising the steps of: a) identifying a model parameter vector of a controlled object model which is obtained by modeling said plant, based on an input and an output of said plant; and b) controlling said plant with a sliding mode control using the model parameter vector identified by said identifying means; said step a) comprising the steps of: i) calculating an identifying error of the model parameter vector; and ii) correcting the identifying error in a decreasing direction, if the identifying error is in a predetermined range; wherein the model parameter vector is calculated using the corrected identifying error.
 26. A control method according to claim 25, wherein the identifying error is set to “0”, if the identifying error is in the predetermined range.
 27. A control method according to claim 25, wherein the predetermined range is set according to an amount of change in a control target value or an amount of change in the output of said plant.
 28. A control method according to claim 25, further comprising the step of calculating a predicted value of the output of said plant, wherein the calculated predicted value is used in controlling said plant with the sliding mode control.
 29. A control method according to claim 28, wherein the predicted value is calculated using the identified model parameter vector.
 30. A control method according to claim 25, wherein said plant includes a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating said throttle valve, and a parameter for determining a control input to be applied to said throttle valve actuating device is calculated in said step b) to make the opening of said throttle valve coincide with a target opening.
 31. A control system for a plant, comprising: an identifying module for identifying a model parameter vector of a controlled object model which is obtained by modeling said plant, based on an input and an output of said plant; and a sliding mode controller for controlling said plant using the model parameter vector identified by said identifying module; said identifying module comprising: an identifying error calculating module for calculating an identifying error of the model parameter vector; an updating vector calculating module for calculating an updating vector according to said identifying error; and an updating vector correcting module for correcting said updating vector by multiplying a past value of at least one element of said updating vector by a predetermined value which is greater than 0 and less than 1; wherein said identifying module calculates the model parameter vector by adding the corrected updating vector to a reference vector of the model parameter vector.
 32. A control system according to claim 31, wherein said updating vector correcting module does not multiply one of an element of the updating vector which is relevant to the input of said plant and an element of the updating vector which is irrelevant to the input and the output of said plant, by the predetermined value.
 33. A control system according to claim 31, wherein said updating vector correcting module multiplies at least one element of the reference vector by the predetermined value.
 34. A control system according to claim 31, wherein said identifying module identifies the model parameter vector using a fixed gain algorithm.
 35. A control system according to claim 31, wherein said identifying error calculating module performs a low-pass filtering of the identifying error and outputs the identifying error after the low-pass filtering.
 36. A control system according to claim 31, further comprising predicting module for calculating a predicted value of the output of said plant, wherein said sliding mode controller uses the calculated predicted value.
 37. A control system according to claim 36, wherein said predicting module calculates the predicted value using the model parameter vector identified by said identifying module.
 38. A control system according to claim 31, wherein a control input applied from said sliding mode controller to said plant includes an adaptive law input.
 39. A control system according to claim 31, wherein said plant includes a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating module for actuating said throttle valve, and said sliding mode controller calculates a parameter for determining a control input to be applied to said throttle valve actuating device to make the opening of said throttle valve coincide with a target opening.
 40. A control system for a plant, comprising: an identifying module for identifying a model parameter vector of a controlled object model which is obtained by modeling said plant, based on an input and an output of said plant; and a sliding mode controller for controlling said plant using the model parameter vector identified by said identifying module; said identifying module comprising: an identifying error calculating module for calculating an identifying error of the model parameter vector; and an identifying error correcting module for correcting the identifying error in a decreasing direction, if the identifying error is in a predetermined range; wherein said identifying module calculates the model parameter vector using the identifying error corrected by said identifying error correcting module.
 41. A control system according to claim 40, wherein said identifying error correcting module sets the identifying error to “0”, if the identifying error is in the predetermined range.
 42. A control system according to claim 40, wherein the predetermined range is set according to an amount of change in a control target value or an amount of change in the output of said plant.
 43. A control system according to claim 40, further comprising predicting module for calculating a predicted value of the output of said plant, wherein said sliding mode controller uses the calculated predicted value.
 44. A control system according to claim 43, wherein said predicting module calculates the predicted value using the model parameter vector identified by said identifying module.
 45. A control system according to claim 40, wherein said plant includes a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating module for actuating said throttle valve, and said sliding mode controller calculates a parameter for determining a control input to be applied to said throttle valve actuating device to make the opening of said throttle valve coincide with a target opening. 